NAIS Journal vol.6
発刊に際してSwarm Intelligence
David Bradford and Chih-Cheng Hung
Biometric Recognition Research Center
Southern Polytechnic State University, Marietta, GA 30060-2896 USA■ Introduction ■problem solving by imitating them? Not imitating them in a
broad sense, but instead in a direct way, by encoding their

Swarm Intelligence. Does that phrase bring to mind theactions and behaviors and using that encoding to solve

1978 movie “The Swarm” in which deadly African bees（figure

problems – and not just generic problems but problems that1）spread through the US killing thousands? Or perhaps

could have many variables and many conditions that might

it spurs memories of the 1954 movie “Them!” about ants

affect any eventual best solution or solutions.

（mutated by atomic testing）that threaten civilization? No?
Maybe the phrase swarm intelligence evokes images of insects

Computer scientists withworking together to create works of surprising size and shape

the invaluable collaboration– giant termite mounds（figure 2）or other complex nestingof scientists in a multitude ofstructures? Perhaps it brings to mind the ability of a group ofdifferent disciplines have been,

insects to behave as if they sharein recent years, creating models

some psychic connection thatthat are inspired by these natural

指されてきた情報システム（Information Systems）の研究領域の多様性，

allows them to work together toswarms. They are modeling the

ビジネス性，グローバル化の重視という視点をよりよく発展させるために，

achieve success over an obstacle

behaviors and actions carriedor situation? Whether the termout by natural swarms using

swarm intelligence brings to mind

algorithms. That’s right, using

movies or the social interactions

algorithms as in figure 3; a seriesof the insects involved, whatof steps to accomplish a task –いスピードで進む日本の少子化・高齢化（人口の波）の問題が，廉価な商

both images share is the concept

but not just a single point a to point b algorithm, like washing

品ばかりを市場に溢れさせるデフレ・スパイラルや大学新卒者の６割程度

of a group of entities achieving

hair（wet hair, apply shampoo, lather, rinse, repeat）, butmore together than they could

algorithms that have many variables and many conditionsindividually.

whose solutions may affect a variety of actions; something

NAIS 支部長

手塚 正義

情報システム学会日本支部（NAIS）の支部長を 2010 年 11 月にお引き受けした京都情報大学院大学教授の手塚です。NAIS Journal vol.6 の発刊に際してご挨拶申し上げます。これまで前支部長の長谷川利治先生が目NAIS を取り巻く実学環境（新事業応用，社会生活支援などの分野）で具体的な連携テーマを探っていきたいと考えています。最近の報道は，『ジャパンシンドローム』という言葉をよく取り上げています。世界に類を見なしかない就職内定率という雇用不安さを増大させていると分析しています。この状況を打破する処方箋は，付加価値の高い日本固有ブランドの形

Figure 1 A swarm of bees
［Fir0002/Flagstaffotos, 2006］.

Figure 3 A simple algorithm［BrokenSegue, 2007］.

like putting together a travel route between lots of cities

成です。その達成には，世界トップレベルの技術者・研究者が切磋琢磨すThe successes that nature-

where there are so many cities to visit and only a certain

る研究開発環境を国内に用意することを急がねばなりません。NAIS におbased swarm intelligences

ordering of visits makes cost-effective sense or monitoring a

ける学会・研究活動もこれまでの枠を超えて多方面との連携を深め，我々

achieve are only successes

nuclear reactor and adjusting the energy output to meet thewhen we apply an idea of anenergy demands of the moment without waste or damage –obstacle to be overcome –such tasks whose hands-on solutions can take quite a bit ofand not just any obstacle buttime to calculate.

の固有ブランド力を向上させ，差別化を図った厚利小売のビジネスモデルの創造に寄与する道をしっかり歩みたいと考えています。

one that would seeming ly

ＡＩＳ（情報システム学会）は，本部を米国ジョージア州立大学に置き，オースト

be beyond the capacity of a

Natural swarms, in an intelligent way that seems beyond

ラリア・ニュージーランド，モロッコ，パキスタン，スロベニア，中国語圏，アイ

single individual of the swarm

the means of its individuals, appear to be able to solve theseto grasp or solve. When thesetypes of issues quickly and efficiently（finding a short pathルランド，イタリアなどに支部を持つ国際的な学会であり（http：//www.aisnet.
org/），ＩＳ（Information System，情報システム）全般を研究領域としています。

Figure 2 A termite mound［Yap, 2005］.

through a circuitous route to a food supply, or monitoring

together to solve a problem -the population, temperature, honeycomb size and movements

特に，企業における情報システムの管理手法，企業内のＩＴ活動に関する方法論，

when a group of ants build a living bridge to cross a landscape– analogies to the earlier presented problems）. Computere ビジネス／ウェブビジネスについての研究活動などを展開しています。会員は全hindrance（whether water or a gap between two limbs inscientists are now modeling algorithms inspired by naturala tree）, when bees harvest pollen from a plentiful source

swarms that get as close as possible to producing the bestinstead of wasting time pursuing poor sources, then their

attainable solutions to solve these types of difficult tasks in an

resulting solution benefits the whole swarm. Since nature-efficient and timely manner! Imitation by algorithmic encoding

based swarms are so successful, can we understand andof natural swarm behavior creates/calculates solutions that

achieve something new, something also beneficial, about

appear to have been thought-up by the algorithmic swarm

世界で 4000 名を有し，今後さらなる発展が期待されています。

individual elements workand to have been agreed upon by its members as the solution

formation and self organization, so too do Craig Reynolds’“as a multi-agent approach to difficult combinatorial

through the problem landscape toward the best-discovered

best suitable to the problem at hand; thus the idea of swarm

Boids display, through the calculation of simple rules, apparent

optimization problems like the traveling salesman problem

5

solution, calculating a solution that all particles of the swarm

and the quadratic assignment problem ［Dorigo, Di Caro,seem to have also found, thus giving the appearance that

& Gambardella, 999］”. In this algorithm the ants solve

they intelligently converged upon a commonly agreed upon

portions of a given problem then assemble only the bestsolution.

intelligence in algorithms.

self organization and intelligent tendencies of crash avoidance.

■ 1989 ■■ Pioneering Work ■Gerardo Beni with Jin Wang, in 989, first coined thesolution portions by combining the ones having the strongest

Being by no means an exhaustive listing of every scientist

term “swarm intelligence” when working together in 989

pheromone trail . The swarm in this case communicates its

involved in swarm intelligence research and development, theon cellular robotics［Wikipedia, Gerardo_Beni］. In theirbest solutions by attracting more ants toward the stronger

In 005 at Cardiff University, “researchers at thefollowing researchers and cited works are highlights that arearticle “Swarm Intelligence in Cellular Robotic Systems” theypheromone trail（implicit is the abandonment of those trails/

Manufacturing Engineering Center（MEC）developed the”

used as stepping-stones to illustrate, broadly, the emergence

write（about swarm intelligence）“systems of non-intelligent

solutions that are less reinforced）so that convergence toward［Caridff University］Bees Algorithm. While the algorithm itself

and investigation of swarm based algorithms, from past to

robots exhibiting collectively intelligent behavior evident in

the best possible total solution, for example the shortest routeis of the year 005, the interpretation of bees as a swarm

present（009）.

the ability to unpredictably produce ʻspecific’（i.e. not in a

to a food source, can happen quickly. This quick convergencesystem was mentioned as early as 999 in the book “Swarm

statistical sense）ordered patterns of matter in the external

illustrates the calculation of an answer from numbers ofIntelligence： From Natural to Artificial Systems”［Bonabeau,■ 1970 ■78■ 2005 ■environment”［Beni & Wang, 989］. This definition implies

individuals who by themselves do not calculate the answer as

Theraulaz, & Dorigo, 999］. Based on the waggle-dance ofIn the year 970 John Conway created “thethat Conway and Reynolds, in creating their examples througha whole. Instead each is contributing their solution portion andhoneybees, this algorithm exploits the communication between

best know example of cellular automaton”

code that self organize and create patterns unpredictably,

producing an eventual swarm-determined solution thus giving

foraging members of a swarm to glean information about thewere in fact executing an early form of swarm intelligence;

the idea that the swarm as a whole is exhibiting an intelligencelocation and quantity of pollen（a solution）in order to sendGame of Life . A cellular automaton, it uses a

however, these swarms were not designed as solution seeking

that is greater than that of any individual.

an increased number of swarm-mates back to those solutions

grid structure and assigns each grid cell an on or off statealgorithms for any array of given problems – not directly.

［Wikipedia, Conway's Game of Life with the（either shaded or clear）based on a number of rules. Conway’s Game of Life, with its simple rules and visually appealing

So, in these early years, these first few pioneers led therepresentation allowing user interaction “provides an example

way in conceptualizing and defining an idea of swarms ofof emergence and self organization… because of thesurprising ways in which patterns can evolve”［Wikipedia,

individuals that do something intelligent so that discernable,
sometimes unpredictable, patterns emerge.

Conway's Game of Life］. While cellular automaton are not［５］Given a list of cities and their pairwise distances, the task is to find a shortest possible
tour that visits each city exactly once.［Wikipedia, Traveling Salesman Problem］［６］A set of n activities/items must be assigned to n locations/resources in such a waythat a cost function of the couplings is minimized.
［７］The pheromone trail is a chemical deposit left by ants that is ʻattractiveʼ to others
ants but which has a limited ʻlifespanʼ and evaporates if not reinforced by that same orother ants as they travel.
［８］In this context, convergence is the gathering of individuals of a swarm around acertain solution value（or values）.

■ 1995 ■that are most promising while other members continue theirsearch. In this way the bee swarm quickly approaches the bestpollen supply or supplies and can appear to have intelligently
found the overall best solution（s）.

■ What is swarm intelligence? ■specifically a swarm intelligence, the methods used and theJ. M. Bishop, also in 989, first elaborated Stochastic

ʻseemingly intelligent’ evolution of some patterns speaks to

Diffusion searching in his paper “Stochastic Searching

the general history leading toward swarm intelligence studies.

Networks.” He elaborates a method of pattern-matching

Russell Eberhart（an electrical engineer）, in 995, produced

behavior seemingly observed in schooling fish or flocking

using cells having randomized values that, when successfully

a paper “Particle Swarm Optimization” in which they proposed

birds is different from that of ants and bees, which ismapped to a solution then diffuses their successful solution

the particle swarm optimization algorithm. By blending theiralso different from the actual implementation of a swarm

to those mapped cells that have invalid values. This may be

two disciplines they conceived of an algorithm that would haveintelligent algorithm. Each does, however, share some common

considered the first of the swarm intelligent algorithms as

an individual solution（an individuals’ solution which would

characteristics that allow them to be grouped under thethe individual values, the ants or bees if you will, which arebecome the best overall solution if conditions warranted）

umbrella-term of swarm intelligence.

［１］http：//www.bitstorm.org/gameoﬂife/［２］Pronounced ʻought-TOM-ah-tawnʼ, meaning self-operating, moving automaticallywithout outside interference.
［３］Emergence is the way complex systems and patterns arise out of a multiplicity ofrelatively simple interactions.［Wikipedia, Emergence］■ 1986 ■4James Kennedy（a social psychologist）andIs swarm intelligence easy to define, does it have one
definition? Assuredly not, in both cases. The intelligent

considered active（aka successful）must be communicated toand a group best solution（a best overall solution）. TheIn 986 Craig Reynolds

the other solutions which are considered inactive（that do notindividuals would search for solutions between their best

created “a computer model

yet have a successful mapping to a valid solution）. Without

solution and the groups’（termed a swarm）best solution so

The components of any living swarm tend to be self-of coordinated animal motion

being a literal interpretation of any particular nature-inspiredthat any solution found by one individual that was the bestorganizing meaning that they are able to make independent

such as bird flocks and fishswarm, this swarm of solutions achieves a final outcome

solution would attract the other individuals to search in thatdecisions based on their surroundings and behave accordingly

schools”［Reynolds］whichquickly because of sharing successfullyʻbest’ solution area. This would only be possible if there wereto the stimulus they encounter. They don’t crash into eachhe called Boids. His creation

mapped values/information -（in the casea number of individuals, termed particles, who communicatedother, unless circumstances arise that allow that action, andwas similar to, but differentof Mr. Bishop’s paper, a matched pattern ofamongst themselves their solutions so that a best overall

they cooperate for the greater benefit of their swarm by

from, cellular automatons. His Boids（represented in threenumbers is found）.

solution/direction could be pursued.

performing necessary tasks as dictated by their environment

dimensions instead of using a two dimensional grid-based［４］Pronounced ʻstow-KAS-tickʼ, meaning random.

visualization）also follow simple rules. These animated specks,
as seen on his website： http：//www.redd.com/cwr/boids/,

4

6■ 1992 ■■ In nature： ■9and their current role.
This particle swarm, while not directly taking inspirationfrom insects in the way that Ant Colony Optimization［４］Swarm describes a behaviour of an aggregate of animals of similar size and bodyorientation, often moving en masse in the same direction.［Wikipedia, Swarm］

can be said to be a swarm implementation of individuals which

The first true nature-inspired swarm algorithm can bedoes, can be deemed a fully connected social-entity where

In this natural sense then the definition of a swarm of

exhibit many of the same coordinated movements of the life

claimed by Ant Colony Optimization and was introduced

individuals determine their own solutions to the problem

insects is also a flock of birds, a shoal of fish, a herd of land

forms they represent（namely birds or fish）. In the same way

by Marco Dorigo’s Phd thesis “Optimization, Learning andand then agree upon a solution as found by the whole swarm

animals, a pod of whales – in essence any “collective motion ofthat John Conway’s Game of Life displays examples of pattern

Natural Algorithms”（in the year 99）. It was proposedas their best solution. In this way the swarm moves quicklya large number of self-propelled entities”［Wikipedia, Flocking

5and to have been agreed upon by its members as the solution

formation and self organization, so too do Craig Reynolds’“as a multi-agent approach to difficult combinatorial

through the problem landscape toward the best-discovered

best suitable to the problem at hand; thus the idea of swarm

Boids display, through the calculation of simple rules, apparent

optimization problems like the traveling salesman problem

5

solution, calculating a solution that all particles of the swarm

and the quadratic assignment problem ［Dorigo, Di Caro,seem to have also found, thus giving the appearance that

& Gambardella, 999］”. In this algorithm the ants solve

they intelligently converged upon a commonly agreed upon

portions of a given problem then assemble only the bestsolution.

intelligence in algorithms.

self organization and intelligent tendencies of crash avoidance.

■ 1989 ■■ Pioneering Work ■Gerardo Beni with Jin Wang, in 989, first coined thesolution portions by combining the ones having the strongest

Being by no means an exhaustive listing of every scientist

term “swarm intelligence” when working together in 989

pheromone trail . The swarm in this case communicates its

involved in swarm intelligence research and development, theon cellular robotics［Wikipedia, Gerardo_Beni］. In theirbest solutions by attracting more ants toward the stronger

In 005 at Cardiff University, “researchers at thefollowing researchers and cited works are highlights that arearticle “Swarm Intelligence in Cellular Robotic Systems” theypheromone trail（implicit is the abandonment of those trails/

Manufacturing Engineering Center（MEC）developed the”

used as stepping-stones to illustrate, broadly, the emergence

write（about swarm intelligence）“systems of non-intelligent

solutions that are less reinforced）so that convergence toward［Caridff University］Bees Algorithm. While the algorithm itself

and investigation of swarm based algorithms, from past to

robots exhibiting collectively intelligent behavior evident in

the best possible total solution, for example the shortest routeis of the year 005, the interpretation of bees as a swarm

present（009）.

the ability to unpredictably produce ʻspecific’（i.e. not in a

to a food source, can happen quickly. This quick convergencesystem was mentioned as early as 999 in the book “Swarm

statistical sense）ordered patterns of matter in the external

illustrates the calculation of an answer from numbers ofIntelligence： From Natural to Artificial Systems”［Bonabeau,■ 1970 ■78■ 2005 ■environment”［Beni & Wang, 989］. This definition implies

individuals who by themselves do not calculate the answer as

Theraulaz, & Dorigo, 999］. Based on the waggle-dance ofIn the year 970 John Conway created “thethat Conway and Reynolds, in creating their examples througha whole. Instead each is contributing their solution portion andhoneybees, this algorithm exploits the communication between

best know example of cellular automaton”

code that self organize and create patterns unpredictably,

producing an eventual swarm-determined solution thus giving

foraging members of a swarm to glean information about thewere in fact executing an early form of swarm intelligence;

the idea that the swarm as a whole is exhibiting an intelligencelocation and quantity of pollen（a solution）in order to sendGame of Life . A cellular automaton, it uses a

however, these swarms were not designed as solution seeking

that is greater than that of any individual.

an increased number of swarm-mates back to those solutions

grid structure and assigns each grid cell an on or off statealgorithms for any array of given problems – not directly.

［Wikipedia, Conway's Game of Life with the（either shaded or clear）based on a number of rules. Conway’s Game of Life, with its simple rules and visually appealing

So, in these early years, these first few pioneers led therepresentation allowing user interaction “provides an example

way in conceptualizing and defining an idea of swarms ofof emergence and self organization… because of thesurprising ways in which patterns can evolve”［Wikipedia,

individuals that do something intelligent so that discernable,
sometimes unpredictable, patterns emerge.

Conway's Game of Life］. While cellular automaton are not［５］Given a list of cities and their pairwise distances, the task is to find a shortest possible
tour that visits each city exactly once.［Wikipedia, Traveling Salesman Problem］［６］A set of n activities/items must be assigned to n locations/resources in such a waythat a cost function of the couplings is minimized.
［７］The pheromone trail is a chemical deposit left by ants that is ʻattractiveʼ to others
ants but which has a limited ʻlifespanʼ and evaporates if not reinforced by that same orother ants as they travel.
［８］In this context, convergence is the gathering of individuals of a swarm around acertain solution value（or values）.

■ 1995 ■that are most promising while other members continue theirsearch. In this way the bee swarm quickly approaches the bestpollen supply or supplies and can appear to have intelligently
found the overall best solution（s）.

■ What is swarm intelligence? ■specifically a swarm intelligence, the methods used and theJ. M. Bishop, also in 989, first elaborated Stochastic

ʻseemingly intelligent’ evolution of some patterns speaks to

Diffusion searching in his paper “Stochastic Searching

the general history leading toward swarm intelligence studies.

Networks.” He elaborates a method of pattern-matching

Russell Eberhart（an electrical engineer）, in 995, produced

behavior seemingly observed in schooling fish or flocking

using cells having randomized values that, when successfully

a paper “Particle Swarm Optimization” in which they proposed

birds is different from that of ants and bees, which ismapped to a solution then diffuses their successful solution

the particle swarm optimization algorithm. By blending theiralso different from the actual implementation of a swarm

to those mapped cells that have invalid values. This may be

two disciplines they conceived of an algorithm that would haveintelligent algorithm. Each does, however, share some common

considered the first of the swarm intelligent algorithms as

an individual solution（an individuals’ solution which would

characteristics that allow them to be grouped under thethe individual values, the ants or bees if you will, which arebecome the best overall solution if conditions warranted）

umbrella-term of swarm intelligence.

［１］http：//www.bitstorm.org/gameoﬂife/［２］Pronounced ʻought-TOM-ah-tawnʼ, meaning self-operating, moving automaticallywithout outside interference.
［３］Emergence is the way complex systems and patterns arise out of a multiplicity ofrelatively simple interactions.［Wikipedia, Emergence］■ 1986 ■4James Kennedy（a social psychologist）andIs swarm intelligence easy to define, does it have one
definition? Assuredly not, in both cases. The intelligent

considered active（aka successful）must be communicated toand a group best solution（a best overall solution）. TheIn 986 Craig Reynolds

the other solutions which are considered inactive（that do notindividuals would search for solutions between their best

created “a computer model

yet have a successful mapping to a valid solution）. Without

solution and the groups’（termed a swarm）best solution so

The components of any living swarm tend to be self-of coordinated animal motion

being a literal interpretation of any particular nature-inspiredthat any solution found by one individual that was the bestorganizing meaning that they are able to make independent

such as bird flocks and fishswarm, this swarm of solutions achieves a final outcome

solution would attract the other individuals to search in thatdecisions based on their surroundings and behave accordingly

schools”［Reynolds］whichquickly because of sharing successfullyʻbest’ solution area. This would only be possible if there wereto the stimulus they encounter. They don’t crash into eachhe called Boids. His creation

mapped values/information -（in the casea number of individuals, termed particles, who communicatedother, unless circumstances arise that allow that action, andwas similar to, but differentof Mr. Bishop’s paper, a matched pattern ofamongst themselves their solutions so that a best overall

they cooperate for the greater benefit of their swarm by

from, cellular automatons. His Boids（represented in threenumbers is found）.

solution/direction could be pursued.

performing necessary tasks as dictated by their environment

dimensions instead of using a two dimensional grid-based［４］Pronounced ʻstow-KAS-tickʼ, meaning random.

visualization）also follow simple rules. These animated specks,
as seen on his website： http：//www.redd.com/cwr/boids/,

4

6■ 1992 ■■ In nature： ■9and their current role.
This particle swarm, while not directly taking inspirationfrom insects in the way that Ant Colony Optimization［４］Swarm describes a behaviour of an aggregate of animals of similar size and bodyorientation, often moving en masse in the same direction.［Wikipedia, Swarm］

can be said to be a swarm implementation of individuals which

The first true nature-inspired swarm algorithm can bedoes, can be deemed a fully connected social-entity where

In this natural sense then the definition of a swarm of

exhibit many of the same coordinated movements of the life

claimed by Ant Colony Optimization and was introduced

individuals determine their own solutions to the problem

insects is also a flock of birds, a shoal of fish, a herd of land

forms they represent（namely birds or fish）. In the same way

by Marco Dorigo’s Phd thesis “Optimization, Learning andand then agree upon a solution as found by the whole swarm

animals, a pod of whales – in essence any “collective motion ofthat John Conway’s Game of Life displays examples of pattern

Natural Algorithms”（in the year 99）. It was proposedas their best solution. In this way the swarm moves quicklya large number of self-propelled entities”［Wikipedia, Flocking

5behavior］. They, the natural swarms, therefore all share

So how can all those earlier stated properties of a natural

these properties： there are many, relatively homogenous,

swarm be interpreted so that they may be used as building

individuals who interact with each other and their environment

blocks of a swarm-based algorithm? Here are 5 principles for（using positive and negative reinforcement）to form aalgorithmic swarms：“The ant colony optimization algorithm is a probabilistic“A problem is given, and some way to evaluate a proposed

stable and cohesive pattern making whole［Scholarpedia］

.

Proximity – The population should be able to carry out

technique for solving computational problems which can be

solution to it exists in the form of a fitness function. A

simple space and time computations.

reduced to finding good paths through graphs”［Answers.com

communication structure or social network is also defined,

Quality – The population should be able to respond to

website］. The algorithm works with nodes, aka locations, thatassigning neighbors for each individual to interact with. Thenquality factors in the environment.

an ant can travel to that have an associated ʻcost’ - travel to aa population of individuals defined as random guesses at theDiverse Response – The population should not commit its

good node may have a low cost where to a bad node may haveproblem solutions is initialized. These individuals are candidate

activity along excessively narrow channels.

a high cost. As the ants travel from node to node（and not all

solutions. They are also known as the particles, hence theStability – The population should not change its mode ofants must travel to all possible nodes）those node-connections

name particle swarm. An iterative process to improve thesebehavior every time the environment changes.

that incur the least cost are termed the optimal, or best, paths.

candidate solutions is set in motion. The particles iteratively

Adaptability – the population must be able to change

In this way the shortest paths that have been traveled by any

evaluate the fitness of the candidate solutions and remember［Hoffmeyer］. Others definitions, as a sample, are available

behavior mode when it’s worth the computational price.

ant can be assembled to represent the single shortest path,the location where they had their best success. The individual's

from［White］and［Google, swarm intelligence］.

（points -5 as quoted from Mark Millonas by［Eberhart,

and without any one ant necessarily traveling the entire route

best solution is called the particle best or the local best. Each

Shi, & Kennedy, 00］）from starting node to ending node. The emergent intelligenceparticle makes this information available to their neighbors.

of the swarm solution is the shortest overall path/solution

They are also able to see where their neighbors have had

attained by its members.

success. Movements through the search space are guided by［Liu & Passino, 000］［Beni, Order by Disordered Action in
Swarms］［Krink］. When these natural swarms’ properties.

are viewed as a whole then a definition of swarm intelligencecan be formulated; many variations such a definition exist,.

one being： “A swarm has been defined as a set of（mobile）
agents which are liable to communicate directly or indirectly

4.

（by acting on their local environment）with each other, andwhich collectively carry out a distributed problem solving”■ In algorithms：■

5.

A swarm algorithm’s intelligence is directly proportional toquickly, the longer routes are not attractive）.

■ Particle Swarm Optimization（PSO） ■PSO is best described by：

The idea of a natural swarm being intelligent, or not, is

its ability to produce patterns of solutions to a problem thatan externally imposed human-based interpretation applied

can be understood as emergent, that is, understood/perceived

to the actions and/or results achieved by the individuals ofby an entity external to the swarm as a collaboration of values

In setting up the algorithm, a programmer would encode thethe end of a trial, on a problem solution better than that ofthat natural swarm when such are seemingly beyond thewhose calculated solutions, achieved without explicit direction

number of ants and number of nodes, the costs associated tonon-swarm approach using the same methods.”［Answers,

capabilities of its members independently. In those cases thefrom any single entity of the algorithmic swarm, are presented

reach each and any other node specific information, including

particle-swarm-optimization］

intelligence observable is termed to be emergent. This idea

as a single collective whole. Normally, a swarm algorithm

any constraints involved（e.g. node 5 can only be reached fromis equally applicable to a swarm intelligent algorithm. The

takes only one facet of a swarm’s activity and interprets that

node ）, the amount of attractive pheromone that an ant

PSO’s emergent intelligence is its ability to take an objective

intellectual jump from nature-based swarms to an algorithmic

facet – e.g. bringing food back to the nest. A swarm intelligent

should deposit（to lure other ants toward its better solution）

function and, without any one particle defined as a permanent

counterpart can be seen in the following quote：algorithm does not try to also focus on building of the nest

and the evaporation rate of that pheromone, and an algorithm

leader, arrive as a collective swarm at a solution fitting thethese successes, with the population usually converging, by“a social insect colony is undoubtedly a decentralized

or storage of food in the nest nor any other division of labortermination condition.（paraphrased/compiled from：［Dorigo

objective. In setting up the algorithm a programmer would

problem-solving system, comprised of many relativelyof the entities. Because of this single-facet-focus a swarm& Di Caro, 999］）

encode the number of particles, the termination condition,

simple interacting entities.” “the modeling of social

algorithm may have a pared-down architecture（a more

the objective function whose calculation results in a fitness,

insects by means of SO（self organization）can help

restrictive set of rules for use）that could make the emergence［13］Any algorithm that works for all practical purposes but has a theoretical chance ofbeing wrong.［NIST, probabilistic algorithm］

design artificial distributed problem-solving devices

of intelligence in its pursuit of a solution less obvious.

that self-organize to solve problems – swarm intelligent

However, this focused approach allows for a swarm algorithm

systems.”［Bonabeau, Theraulaz, & Dorigo, 999］

to be encoded in such a way as to return potentially more

0

Therefore, looking at a natural swarm as ʻsimple interacting

reliable results that occur in the range expected.

algorithm having multiple variables（the swarm）that take■ Types of swarm intelligence ■on solution values appropriate to the problem at hand . Thesevariables may compare their values with each other, build a■ Bee Algorithms ■The Bees Algorithm is an optimization algorithm inspired by
the natural foraging behavior of honey bees to find the optimalor cost, value of each solution so that a determination canbe made as to that solution being better than the particle
or swarms solution so far, and the particles communication
method（via neighbors or through a single particle or allparticles）.

solution, i.e. most plentiful food source. The algorithm requires

entities that problem solve’ is easily re-interpreted to be an

6

the longer routes（since the enticing pheromones evaporate■ Ant Colony Optimization（ACO） ■a number of parameters to be set, namely： number of scout■ Swarm intelligence and traditional optimization tradeoﬀ ■bees（n）, number of sites selected out of n visited sites（m）,
number of best sites out of m selected sites（e）, number

You might wonder what you gain by using a swarm

best value found-so-far collection and, without individually

The image in Figureof bees recruited for best e sites（nep）, number of bees

intelligent algorithm, or, on the flip side of the coin, what you

knowing the solution, arrive as a whole at a solution that is

4 is one of the classic

recruited for the other（m-e）selected sites（nsp）, initial size

lose. Well, the gains and losses aren’t easy to measure since

suitable for the algorithmic swarms’ objective（i.e. the problem

interpretations of howof patches（ngh）which includes site and its neighborhood andany win against a particular problem is possibly not a win

at hand）.

an ant colony quickly

stopping criterion.［Answers, Bees-algorithm］/ ［Wikipedia,

against another problem. What we mean is that use of a swarm［９］Swarm describes a behaviour of an aggregate of animals of similar size and bodyorientation, often moving en masse in the same direction.［Wikipedia, Swarm］［10］Self-organization is a process of attraction and repulsion in which the internalorganization of a system, normally an open system, increases in complexity without being
guided or managed by an outside source. Self-organizing systems typically（but notalways）display emergent properties.［Google, self organization］［11］To ʻtake on solution valuesʼ is to perform calculation（s）on the variable value
and evaluate the value arrived at for appropriateness as a valid possible answer to theobjective problem being solved. This process is repeated for each ʻentity of the swarmʼ
for as long as the swarm is tasked to solve the problem（the swarmsʼ lifespan）.
［12］Where ʻthe problem at handʼ is the problem landscape（that range of valid
solutions）defined by the objective the swarm is assigned to achieve/solve.

finds the shortest

Bees-algorithm］

intelligent algorithm should be properly weighed against theroute between a foodFigure 4 Finding the shortest path using pheromone
reinforcement for the shortest path［Nojhan, 2006］.

type of problems you are hoping that algorithm will solve

source（F）and their

The emergent intelligence involved is the ability of theas well as any necessity for speed（to find any acceptable

nest（N）. Stigmergy,

swarm to locate a superior solution source and quickly

solution quickly）versus absolute certainty of perfect, precise,solution.

the reinforcement

optimize that source; should the source become exhausted

of a pheromone trail, is the active responsible agent which（or the best solution source ʻchange’）then the algorithm canentices the ants to remain on the shortest path and abandon

adapt readily.

“Speed and precision are conflicting objectives, at least

7

behavior］. They, the natural swarms, therefore all share

So how can all those earlier stated properties of a natural

these properties： there are many, relatively homogenous,

swarm be interpreted so that they may be used as building

individuals who interact with each other and their environment

blocks of a swarm-based algorithm? Here are 5 principles for（using positive and negative reinforcement）to form aalgorithmic swarms：“The ant colony optimization algorithm is a probabilistic“A problem is given, and some way to evaluate a proposed

stable and cohesive pattern making whole［Scholarpedia］

.

Proximity – The population should be able to carry out

technique for solving computational problems which can be

solution to it exists in the form of a fitness function. A

simple space and time computations.

reduced to finding good paths through graphs”［Answers.com

communication structure or social network is also defined,

Quality – The population should be able to respond to

website］. The algorithm works with nodes, aka locations, thatassigning neighbors for each individual to interact with. Thenquality factors in the environment.

an ant can travel to that have an associated ʻcost’ - travel to aa population of individuals defined as random guesses at theDiverse Response – The population should not commit its

good node may have a low cost where to a bad node may haveproblem solutions is initialized. These individuals are candidate

activity along excessively narrow channels.

a high cost. As the ants travel from node to node（and not all

solutions. They are also known as the particles, hence theStability – The population should not change its mode ofants must travel to all possible nodes）those node-connections

name particle swarm. An iterative process to improve thesebehavior every time the environment changes.

that incur the least cost are termed the optimal, or best, paths.

candidate solutions is set in motion. The particles iteratively

Adaptability – the population must be able to change

In this way the shortest paths that have been traveled by any

evaluate the fitness of the candidate solutions and remember［Hoffmeyer］. Others definitions, as a sample, are available

behavior mode when it’s worth the computational price.

ant can be assembled to represent the single shortest path,the location where they had their best success. The individual's

from［White］and［Google, swarm intelligence］.

（points -5 as quoted from Mark Millonas by［Eberhart,

and without any one ant necessarily traveling the entire route

best solution is called the particle best or the local best. Each

Shi, & Kennedy, 00］）from starting node to ending node. The emergent intelligenceparticle makes this information available to their neighbors.

of the swarm solution is the shortest overall path/solution

They are also able to see where their neighbors have had

attained by its members.

success. Movements through the search space are guided by［Liu & Passino, 000］［Beni, Order by Disordered Action in
Swarms］［Krink］. When these natural swarms’ properties.

are viewed as a whole then a definition of swarm intelligencecan be formulated; many variations such a definition exist,.

one being： “A swarm has been defined as a set of（mobile）
agents which are liable to communicate directly or indirectly

4.

（by acting on their local environment）with each other, andwhich collectively carry out a distributed problem solving”■ In algorithms：■

5.

A swarm algorithm’s intelligence is directly proportional toquickly, the longer routes are not attractive）.

■ Particle Swarm Optimization（PSO） ■PSO is best described by：

The idea of a natural swarm being intelligent, or not, is

its ability to produce patterns of solutions to a problem thatan externally imposed human-based interpretation applied

can be understood as emergent, that is, understood/perceived

to the actions and/or results achieved by the individuals ofby an entity external to the swarm as a collaboration of values

In setting up the algorithm, a programmer would encode thethe end of a trial, on a problem solution better than that ofthat natural swarm when such are seemingly beyond thewhose calculated solutions, achieved without explicit direction

number of ants and number of nodes, the costs associated tonon-swarm approach using the same methods.”［Answers,

capabilities of its members independently. In those cases thefrom any single entity of the algorithmic swarm, are presented

reach each and any other node specific information, including

particle-swarm-optimization］

intelligence observable is termed to be emergent. This idea

as a single collective whole. Normally, a swarm algorithm

any constraints involved（e.g. node 5 can only be reached fromis equally applicable to a swarm intelligent algorithm. The

takes only one facet of a swarm’s activity and interprets that

node ）, the amount of attractive pheromone that an ant

PSO’s emergent intelligence is its ability to take an objective

intellectual jump from nature-based swarms to an algorithmic

facet – e.g. bringing food back to the nest. A swarm intelligent

should deposit（to lure other ants toward its better solution）

function and, without any one particle defined as a permanent

counterpart can be seen in the following quote：algorithm does not try to also focus on building of the nest

and the evaporation rate of that pheromone, and an algorithm

leader, arrive as a collective swarm at a solution fitting thethese successes, with the population usually converging, by“a social insect colony is undoubtedly a decentralized

or storage of food in the nest nor any other division of labortermination condition.（paraphrased/compiled from：［Dorigo

objective. In setting up the algorithm a programmer would

problem-solving system, comprised of many relativelyof the entities. Because of this single-facet-focus a swarm& Di Caro, 999］）

encode the number of particles, the termination condition,

simple interacting entities.” “the modeling of social

algorithm may have a pared-down architecture（a more

the objective function whose calculation results in a fitness,

insects by means of SO（self organization）can help

restrictive set of rules for use）that could make the emergence［13］Any algorithm that works for all practical purposes but has a theoretical chance ofbeing wrong.［NIST, probabilistic algorithm］

design artificial distributed problem-solving devices

of intelligence in its pursuit of a solution less obvious.

that self-organize to solve problems – swarm intelligent

However, this focused approach allows for a swarm algorithm

systems.”［Bonabeau, Theraulaz, & Dorigo, 999］

to be encoded in such a way as to return potentially more

0

Therefore, looking at a natural swarm as ʻsimple interacting

reliable results that occur in the range expected.

algorithm having multiple variables（the swarm）that take■ Types of swarm intelligence ■on solution values appropriate to the problem at hand . Thesevariables may compare their values with each other, build a■ Bee Algorithms ■The Bees Algorithm is an optimization algorithm inspired by
the natural foraging behavior of honey bees to find the optimalor cost, value of each solution so that a determination canbe made as to that solution being better than the particle
or swarms solution so far, and the particles communication
method（via neighbors or through a single particle or allparticles）.

solution, i.e. most plentiful food source. The algorithm requires

entities that problem solve’ is easily re-interpreted to be an

6

the longer routes（since the enticing pheromones evaporate■ Ant Colony Optimization（ACO） ■a number of parameters to be set, namely： number of scout■ Swarm intelligence and traditional optimization tradeoﬀ ■bees（n）, number of sites selected out of n visited sites（m）,
number of best sites out of m selected sites（e）, number

You might wonder what you gain by using a swarm

best value found-so-far collection and, without individually

The image in Figureof bees recruited for best e sites（nep）, number of bees

intelligent algorithm, or, on the flip side of the coin, what you

knowing the solution, arrive as a whole at a solution that is

4 is one of the classic

recruited for the other（m-e）selected sites（nsp）, initial size

lose. Well, the gains and losses aren’t easy to measure since

suitable for the algorithmic swarms’ objective（i.e. the problem

interpretations of howof patches（ngh）which includes site and its neighborhood andany win against a particular problem is possibly not a win

at hand）.

an ant colony quickly

stopping criterion.［Answers, Bees-algorithm］/ ［Wikipedia,

against another problem. What we mean is that use of a swarm［９］Swarm describes a behaviour of an aggregate of animals of similar size and bodyorientation, often moving en masse in the same direction.［Wikipedia, Swarm］［10］Self-organization is a process of attraction and repulsion in which the internalorganization of a system, normally an open system, increases in complexity without being
guided or managed by an outside source. Self-organizing systems typically（but notalways）display emergent properties.［Google, self organization］［11］To ʻtake on solution valuesʼ is to perform calculation（s）on the variable value
and evaluate the value arrived at for appropriateness as a valid possible answer to theobjective problem being solved. This process is repeated for each ʻentity of the swarmʼ
for as long as the swarm is tasked to solve the problem（the swarmsʼ lifespan）.
［12］Where ʻthe problem at handʼ is the problem landscape（that range of valid
solutions）defined by the objective the swarm is assigned to achieve/solve.

finds the shortest

Bees-algorithm］

intelligent algorithm should be properly weighed against theroute between a foodFigure 4 Finding the shortest path using pheromone
reinforcement for the shortest path［Nojhan, 2006］.

type of problems you are hoping that algorithm will solve

source（F）and their

The emergent intelligence involved is the ability of theas well as any necessity for speed（to find any acceptable

nest（N）. Stigmergy,

swarm to locate a superior solution source and quickly

solution quickly）versus absolute certainty of perfect, precise,solution.

the reinforcement

optimize that source; should the source become exhausted

of a pheromone trail, is the active responsible agent which（or the best solution source ʻchange’）then the algorithm canentices the ants to remain on the shortest path and abandon

adapt readily.

“Speed and precision are conflicting objectives, at least7

in terms of probabilistic algorithms”［Weiss］. This quotecorresponding side of the car to be OK as long as the car does

illustrates one of the core issues for algorithms – do you wantnot ʻcross’ either of the lines – therefore the optimum answer

a blindingly fast algorithm that can answer the given problem

is a range of answers, really, and not a single position within

Swarm intelligence, as applied to algorithms, isn’t such apretty much successfully the majority of the time, or, do you

the demarcated lane. A swarm intelligent algorithm would be

mystery anymore, now is it? We’ve seen that a natural swarm

want an answer to the problem that is as precise and perfectan excellent choice to solve this type of problem since the lane

isn’t anything more than a conglomeration of entities andWhether the term swarm intelligence first brings to mind

as can be achieved regardless of the time involved? A perfectsize/width could change at any moment（and the algorithm’proposed that intelligence in a natural swarm is only our own

the movies or cooperative insect societies, we hope this brief

environment would allow blindingly fast, perfect and precises individuals, the solutions, could adapt to that change easily

ability to recognize patterns whose creation seemingly are

overview of its algorithmic-interpretation has added depth toanswers to a problem, but with optimization algorithms this isand quite quickly）.

beyond the comprehension or direction of a single individual

your understanding of an interesting and innovative method

not yet the case. “Generally, optimization algorithms can be［14］An optimization algorithm is a numerical method or algorithm for finding a value
x such that f（x）is as small（or as large）as possible, for a given function f, possibly withsome constraints on x.［Wikipedia, list of optimization algorithms］［15］An algorithm whose behavior can be completely predicted from the input.［NIST,
deterministice algorithm］

of that swarm. We’ve conducted a brief survey through theof problem solving.

4

5

divided in two basic classes： deterministic and probabilistic
algorithms”［Weiss］.
Simply by their nature, by the very fact that a swarm
intelligent algorithm is comprised of many individuals, and■ Applications of swarm
intelligence algorithms ■those individuals have a certain ʻfreedom of calculation’ inand couldn’t be achieved by the individuals of the swarm
alone, but, when working together as a group create highquality solutions that emerge and appear inevitable.

history of swarm intelligence computation, selecting highlightsfrom those we’ve termed pioneers, which illustrate how swarm■ Appendix A： ■intelligent algorithms may have evolved in our understanding
and estimation. We further discussed some of the hallmarks“some of the practical issues that arise in attempting toof swarm behavior（something akin to the rules of swarm

find the appropriate tool（i.e. algorithm）for a given problem”

interaction）and presented some of the manners and methods［Coool］– all bullets：

the solutions they generate, we can reasonably say that a“Swarm intelligence has applications in decentralized

those behaviors and properties have been transferred/“some of the practical issues that arise in attempting toswarm intelligent algorithm could easily fall into the classcontrols of unmanned vehicles for the military so single

reinterpreted into a selection of swarm based intelligent

find the appropriate tool（i.e. algorithm）for a given problem”

of probabilistic algorithms. The swarm, as a whole, could

operators can control more unmanned vehicles. The use ofalgorithms. We then delved briefly into the optimization issues［Coool］– all bullets：

evaluate the problem and arrive at an answer that is not

swarm intelligence in medical nanobots may also help combat

presented swarm intelligent algorithms, highlighting strength•Unimodal functions. These are functions which have a single

the one true answer every single time; that possibility does

cancer. Swarm intelligence was used in the creation of the（s）and weakness（es）for solving types of problems, andextremum. The archetype of such functions is the dimensional

exist – however, precisely because a swarm does have many

video sequence "Battle of Helm's Deep" in the movie, Lord offollowed that with some example situations in which swarm

quadratic form. Non-quadratic, but still unimodal, functionsindividuals pursuing solutions, there is always the highthe Rings.”［TechFaq］

intelligent algorithms might be and have been successfully

can usually be optimized by making a sequence of quadratic

employed.

approximations. If the matrix of second derivatives of aprobability that one or more individuals will absolutely find（ifnot the one true answer）an answer that is exceedingly well

“Nature-inspired approaches have not only shown theirsuited to the problem, termed an optimal solution. “One of theefficiency in static optimization problems, but were proven

As researchers continue to explore what swarm intelligencemost fundamental principles in our world is the search for an

to be especially robust in dynamic applications, too. This is

is and is not, and that understanding gets imported and•Essentially unimodal functions. Because of noise and otheroptimal state .”［Weiss］

particularly interesting in the looming age of networks ofapplied to optimization problems through swarm intelligent

factors, it is often the case in practice that the simple global

larger scale. Wireless networks, sensor networks, wirelessalgorithms, more and more hybridizations will emerge.

structure of a problem is masked by parasitic local optima. We“…no one optimization algorithm can possibly be efficientsensor networks, Smart Home networks, ubiquitous

Whether these imported modifications and hybridizations

do not regard these local optima as representing importantor even successful in all cases of interest”［Coool］. So, what

computing, and more require self-organization, efficient

will remain termed swarm intelligent algorithms is up to thefeatures of the model and so, if we could somehow smooth theis the trade off using swarm intelligent algorithms? On therouting, optimal parameter settings, and power management.”

researchers and both their goals and interpretations of theirobjective function, we would be more confident that we had

one hand you get, traditionally, faster solutions to problems［Weiss］

end product/algorithm. It stands to reason that if an algorithm

determined the extremum we seek.

maintains or produces a group of probable solutions to a

•Functions that have a small number of significant local

that would take an inordinate amount of time to evaluate. On

quadratic form is known, then special-purpose algorithms canbe used.

the other hand you lose the ability to say that an answer to a

The above quotes are used to illustrate the hallmarks ofproblem from which a solution or set of solutions is mutually

optima. Sometimes the local optima represent significant

problem will always, definitively, be the best answer and may

problems that could be effectively addressed by a swarm

agreed upon by the group as ʻbest’ then that algorithm isfeatures in the model（for example, fundamental ambiguities

in fact, once in a blue moon, be an answer that isn’t that good

intelligent algorithm. They, the problems, use or represent

partaking in swarm intelligence to some degree – even if nocan exist in inverse calculations; the global optimum may not

at all! This is where your knowledge of the problem becomesmany individuals that are all part of a common task. They

discernable pattern emerges（our ability to pick out a pattern

be fundamentally more significant than other local optima）.

of great importance – is it necessary for your purposes（andwork over a range of solutions that are all ʻoptimally’ valid.

is not guaranteed; a pattern may be present and not yet

In this case we should not simply smooth the objective

the problem being considered）to have a penultimate optimal

They must and do take into account various constraints upon

ascertainable or discovered by our inquiries）.

function; we must find these local optima. If their number issolution or can you work with and use an answer that may betheir solutions so that evaluations/calculations do not lead thevery, very good?

swarm out of the desired solution landscape. They are in someSolutions to problems that emerge from a grouping ofrespects time sensitive, requiring an acceptable answer inindividuals, purposefully or by unintentional group synergy,•Functions with signiﬁcant null-space eﬀects. In the event thatallow a natural swarm to be resilient, to survive in the face

the objective function becomes flat in the neighborhood of theof difficulties that may not be known – this type of flexibility

current point, then this flat region represents perturbations

Consider this simple example problem of driving down the7minimal time from a problem that may be dynamic in nature.

road in a car and having to stay between a pair of lines painted

8■ Conclusion ■relatively small, then we might be able to rely on hill climbing
from randomly chosen starting points.

on the road, termed a lane – it is not paramount that the

There are also many academic and research implementationsof the group（to respond to the unknown）allows for an

to the model which have little or no influence on the objective

distance to the left of the car and the left line be exactly theof swarm intelligent algorithms – and many hybridizations andinterpretation of intelligence as the group creates solution-function. In this case it is important to map out these regions

same as the distance between the right side of the car and themodifications that have been investigated for purposes specificpatterns that it couldn’t have planned for previously. Thisand characterize the ambiguities that they represent.

right line; the answer of staying between the lines and in theto the researchers needs.

too is the goal of swarm intelligence in algorithms – to solve•Functions with a huge number of significant local optima.

lane allows for any distance on either side of the lines to their［17］In a state of ﬂux, or, having changes to its requirements on an ongoing basis.

problems whose solution-patterns haven’t before been formed

It may happen that there are a large number of local optima,

9

in terms of probabilistic algorithms”［Weiss］. This quotecorresponding side of the car to be OK as long as the car does

illustrates one of the core issues for algorithms – do you wantnot ʻcross’ either of the lines – therefore the optimum answer

a blindingly fast algorithm that can answer the given problem

is a range of answers, really, and not a single position within

Swarm intelligence, as applied to algorithms, isn’t such apretty much successfully the majority of the time, or, do you

the demarcated lane. A swarm intelligent algorithm would be

mystery anymore, now is it? We’ve seen that a natural swarm

want an answer to the problem that is as precise and perfectan excellent choice to solve this type of problem since the lane

isn’t anything more than a conglomeration of entities andWhether the term swarm intelligence first brings to mind

as can be achieved regardless of the time involved? A perfectsize/width could change at any moment（and the algorithm’proposed that intelligence in a natural swarm is only our own

the movies or cooperative insect societies, we hope this brief

environment would allow blindingly fast, perfect and precises individuals, the solutions, could adapt to that change easily

ability to recognize patterns whose creation seemingly are

overview of its algorithmic-interpretation has added depth toanswers to a problem, but with optimization algorithms this isand quite quickly）.

beyond the comprehension or direction of a single individual

your understanding of an interesting and innovative method

not yet the case. “Generally, optimization algorithms can be［14］An optimization algorithm is a numerical method or algorithm for finding a value
x such that f（x）is as small（or as large）as possible, for a given function f, possibly withsome constraints on x.［Wikipedia, list of optimization algorithms］［15］An algorithm whose behavior can be completely predicted from the input.［NIST,
deterministice algorithm］

of that swarm. We’ve conducted a brief survey through theof problem solving.

4

5

divided in two basic classes： deterministic and probabilistic
algorithms”［Weiss］.
Simply by their nature, by the very fact that a swarm
intelligent algorithm is comprised of many individuals, and■ Applications of swarm
intelligence algorithms ■those individuals have a certain ʻfreedom of calculation’ inand couldn’t be achieved by the individuals of the swarm
alone, but, when working together as a group create highquality solutions that emerge and appear inevitable.

history of swarm intelligence computation, selecting highlightsfrom those we’ve termed pioneers, which illustrate how swarm■ Appendix A： ■intelligent algorithms may have evolved in our understanding
and estimation. We further discussed some of the hallmarks“some of the practical issues that arise in attempting toof swarm behavior（something akin to the rules of swarm

find the appropriate tool（i.e. algorithm）for a given problem”

interaction）and presented some of the manners and methods［Coool］– all bullets：

the solutions they generate, we can reasonably say that a“Swarm intelligence has applications in decentralized

those behaviors and properties have been transferred/“some of the practical issues that arise in attempting toswarm intelligent algorithm could easily fall into the classcontrols of unmanned vehicles for the military so single

reinterpreted into a selection of swarm based intelligent

find the appropriate tool（i.e. algorithm）for a given problem”

of probabilistic algorithms. The swarm, as a whole, could

operators can control more unmanned vehicles. The use ofalgorithms. We then delved briefly into the optimization issues［Coool］– all bullets：

evaluate the problem and arrive at an answer that is not

swarm intelligence in medical nanobots may also help combat

presented swarm intelligent algorithms, highlighting strength•Unimodal functions. These are functions which have a single

the one true answer every single time; that possibility does

cancer. Swarm intelligence was used in the creation of the（s）and weakness（es）for solving types of problems, andextremum. The archetype of such functions is the dimensional

exist – however, precisely because a swarm does have many

video sequence "Battle of Helm's Deep" in the movie, Lord offollowed that with some example situations in which swarm

quadratic form. Non-quadratic, but still unimodal, functionsindividuals pursuing solutions, there is always the highthe Rings.”［TechFaq］

intelligent algorithms might be and have been successfully

can usually be optimized by making a sequence of quadratic

employed.

approximations. If the matrix of second derivatives of aprobability that one or more individuals will absolutely find（ifnot the one true answer）an answer that is exceedingly well

“Nature-inspired approaches have not only shown theirsuited to the problem, termed an optimal solution. “One of theefficiency in static optimization problems, but were proven

As researchers continue to explore what swarm intelligencemost fundamental principles in our world is the search for an

to be especially robust in dynamic applications, too. This is

is and is not, and that understanding gets imported and•Essentially unimodal functions. Because of noise and otheroptimal state .”［Weiss］

particularly interesting in the looming age of networks ofapplied to optimization problems through swarm intelligent

factors, it is often the case in practice that the simple global

larger scale. Wireless networks, sensor networks, wirelessalgorithms, more and more hybridizations will emerge.

structure of a problem is masked by parasitic local optima. We“…no one optimization algorithm can possibly be efficientsensor networks, Smart Home networks, ubiquitous

Whether these imported modifications and hybridizations

do not regard these local optima as representing importantor even successful in all cases of interest”［Coool］. So, what

computing, and more require self-organization, efficient

will remain termed swarm intelligent algorithms is up to thefeatures of the model and so, if we could somehow smooth theis the trade off using swarm intelligent algorithms? On therouting, optimal parameter settings, and power management.”

researchers and both their goals and interpretations of theirobjective function, we would be more confident that we had

one hand you get, traditionally, faster solutions to problems［Weiss］

end product/algorithm. It stands to reason that if an algorithm

determined the extremum we seek.

maintains or produces a group of probable solutions to a

•Functions that have a small number of significant local

that would take an inordinate amount of time to evaluate. On

quadratic form is known, then special-purpose algorithms canbe used.

the other hand you lose the ability to say that an answer to a

The above quotes are used to illustrate the hallmarks ofproblem from which a solution or set of solutions is mutually

optima. Sometimes the local optima represent significant

problem will always, definitively, be the best answer and may

problems that could be effectively addressed by a swarm

agreed upon by the group as ʻbest’ then that algorithm isfeatures in the model（for example, fundamental ambiguities

in fact, once in a blue moon, be an answer that isn’t that good

intelligent algorithm. They, the problems, use or represent

partaking in swarm intelligence to some degree – even if nocan exist in inverse calculations; the global optimum may not

at all! This is where your knowledge of the problem becomesmany individuals that are all part of a common task. They

discernable pattern emerges（our ability to pick out a pattern

be fundamentally more significant than other local optima）.

of great importance – is it necessary for your purposes（andwork over a range of solutions that are all ʻoptimally’ valid.

is not guaranteed; a pattern may be present and not yet

In this case we should not simply smooth the objective

the problem being considered）to have a penultimate optimal

They must and do take into account various constraints upon

ascertainable or discovered by our inquiries）.

function; we must find these local optima. If their number issolution or can you work with and use an answer that may betheir solutions so that evaluations/calculations do not lead thevery, very good?

swarm out of the desired solution landscape. They are in someSolutions to problems that emerge from a grouping ofrespects time sensitive, requiring an acceptable answer inindividuals, purposefully or by unintentional group synergy,•Functions with signiﬁcant null-space eﬀects. In the event thatallow a natural swarm to be resilient, to survive in the face

the objective function becomes flat in the neighborhood of theof difficulties that may not be known – this type of flexibility

current point, then this flat region represents perturbations

Consider this simple example problem of driving down the7minimal time from a problem that may be dynamic in nature.

road in a car and having to stay between a pair of lines painted

8■ Conclusion ■relatively small, then we might be able to rely on hill climbing
from randomly chosen starting points.

on the road, termed a lane – it is not paramount that the

There are also many academic and research implementationsof the group（to respond to the unknown）allows for an

to the model which have little or no influence on the objective

distance to the left of the car and the left line be exactly theof swarm intelligent algorithms – and many hybridizations andinterpretation of intelligence as the group creates solution-function. In this case it is important to map out these regions

same as the distance between the right side of the car and themodifications that have been investigated for purposes specificpatterns that it couldn’t have planned for previously. Thisand characterize the ambiguities that they represent.

right line; the answer of staying between the lines and in theto the researchers needs.

too is the goal of swarm intelligence in algorithms – to solve•Functions with a huge number of significant local optima.

lane allows for any distance on either side of the lines to their［17］In a state of ﬂux, or, having changes to its requirements on an ongoing basis.

problems whose solution-patterns haven’t before been formed

It may happen that there are a large number of local optima,

9many of which are significant. We cannot ignore them byGoogle.（n.d.）. self organization. Retrieved 12 2009, from Wikipedia.org:

smoothing the objective functions, and it may be that random

http://www.google.com/search?q ＝ define % 3Aself-organization&sourceid

hill climbing is too inefficient. This is where Monte Carlo
methods such as Simulated Annealing and Genetic Algorithms＝ ie8-activity&hl ＝ en

•Functions whose global structure provides no usefulglossary_definition&ct ＝ title&ved ＝ 0CAcQkAE
Hoffmeyer, J. Swarm-semiotics. Retrieved 12 2009, from Molbio.ku.dk:
http://www.molbio.ku.dk/MolBioPages/abk/PersonalPages/Jesper/Swarm.

■ 概 要 ■

Krink, T. Swarm Intelligence - Introduction. Retrieved 12 2009, from staff.

インターネットでは，様々な機能の Web アプリケーション

an alternative parameterization can be found in which thewashington.edu: http://staff.washington.edu/paymana/swarm/krink_01.

や Web サービスが公開されている。それらを利用するには，

brute force random searching or enumeration may be the only
alternative.

■ Bibliography ■pdf
Liu, Y., & Passino, K. M.（2000）. Swarm Intelligence: Literature Overview.

サービスの提供を求めることが必要である。それは，プログラ

www.itl.nist.gov/div897/sqg/dads/HTML/deterministicAlgorithm.html

ムの面からは，遠隔メソッドの起動（RMI： Remote Method

NIST. probabilistic algorithm. Retrieved 12 2009, from itl.nist.gov: http://

Invocation あるいは RPC： Remote Procedure Call）によるもNojhan. ACO Branches.

Web サービスの実現の形態を拡張して，今後の Web サービスの発展を考える素材とする。

定 め ら れ た API（Application Program Interface） に 従 っ て

NIST. deterministice algorithm. Retrieved 12 2009, from itl.nist.gov: http://

www.itl.nist.gov/div897/sqg/dads/HTML/probablAlgo.html

形態を実現することの基盤とする。
これら二つの方法がどのように応用できるかは別にして，

html

the function is of no use in finding the desired model. Unlessfunction has some global structure, such as in B, extensive

教授

渡邉 勝正

org: http://www.google.com/search?hl ＝ en&defl ＝ en&q ＝ define:are normally used.

for an isolated, deep optimum, then the global structure of

京都情報大学院大学

Google.（n.d.）. swarm intelligence. Retrieved 12 2009, from Wikipedia.
Swarm+intelligence&ei ＝ RyQsS76sFsz_nAfb6pyCCQ&sa ＝ X&oi ＝

information. If the objective function is essentially flat, except

巡回するオブジェクトによるデータの収集のである。

■ 2. 遠隔メソッドの起動（RMI：Remote Method Invocation）■■ 2.1 RMIの手順 ■RMI は，クライアント（C）とサーバ（S）間の通信のプロ

本論文では，まず，RMI の動きを，一つの例を挙げて述べる。

グラム手順を，ソケットによる場合に比べて簡潔にしている。

Answers. Bees-algorithm. Retrieved 12 2009, from Answers.com: http://com/cwr/boids/

それを基にして，RMI を 2 方向の RMI に拡張して，新しいサー（1s）提供するメソッドをもつオブジェクトインスタンスを，

www.answers.com/topic/bees-algorithm

Scholarpedia. Swarm Intelligence. Retrieved 12 2009, from Scholarpedia.

Answers. particle-swarm-optimization. Retrieved 12 2009, from Answers.

org: http://www.scholarpedia.org/article/Swarm_intelligence

ビス形態の可能性を探ると共に，使用に向けての問題点を明らサーバの名前とキーワード（合言葉）で結び付ける（rebind）。

com: http://www.answers.com/topic/particle-swarm-optimization

TechFaq. swarm-intelligence. Retrieved 12 2009, from tech-faq.com:

かにする。その問題点を解消する一つの方策として，RMI を

これは名前の登録になる。

Answers.com website. Ant Colony Optimization. Retrieved 12 2009, fromhttp://www.tech-faq.com/swarm-intelligence.shtml

用いて，複数のサイトを移動して巡回するオブジェクトの実現（2c）サーバの名前とキーワードによって，メソッドをもつオ

Answers.com: http://www.answers.com/topic/ant-colony-optimization

Weiss, T. Global Optimization Algorithms - Theory and Application.

を示す。これは，たとえば，複数の顧客における発注情報を集ブジェクトインスタンスを探し出す（lookup）。これは名前にBeni, G. Order by Disordered Action in Swarms. Retrieved 12 2009, from

Retrieved 12 2009, from it-weise.de: http://www.it-weise.de/projects/book.

swarm-robotics.org: www.swarm-robotics.org/SAB04/presentations/beni-

pdf

めてくる機能として適用できる。

よる検索で行える。

research.ppt

White, T. Swarm Intelligence. Retrieved 12 2009, from sce.carleton.ca:

Beni, G., & Wang, J.（1989）. Swarm Intelligence in Cellular Robotic

http://www.sce.carleton.ca/netmanage/tony/swarm.html

Systems. Proceedings of NATO Advanced Workshop on Robots andWikipedia. Bees-algorithm. Retrieved 12 2009, from Wikipedia.org: http://

Biological Systems , 102.

en.wikipedia.org/wiki/Bees_algorithm

Bishop, J. M.（1989）. Stochastic searching networks. First IEE

Wikipedia. Conway's Game of Life. Retrieved 12 2009, from Wikipedia.org:

International Conference on Artificial Neural Networks（pp. 329-331）.

http://en.wikipedia.org/wiki/Conway% 27s_Game_of_Life

London: IEEE.

Reynolds, C. Boids. Retrieved 12 2009, from red3d.com: http://www.red3d.

（3c）メソッドの入出力仕様を定義したインタフェースに基づ■ 1. はじめに ■いて，遠隔のメソッドを呼び出す。これは，手元のメソッドを呼び出すのと同じ形になる。

インターネット上には種々の Web サービスあるいは Web

結果の型の変数 ＝ オブジェクト . メソッド（引数のリスト）;

アプリケーション（以降は，「Web サービス」とする）が開発（4s）受け取った引数に対する処理をして，その結果の値を送

Wikipedia. Emergence. Retrieved 12 2009, from Wikipedia.org: http://

されている。広く公開されているもの，または，対象とする利り返す。これは，関数値を変数に代入する文で表現できる。

Bonabeau, E., Theraulaz, G., & Dorigo, M.（1999）. Swarm Intelligence.

en.wikipedia.org/wiki/Emergence

用者を限定して通知しているものがある。通知の方法は違って名前の登録（1s）と検索（2c）は，
rmiregistry に任せる。メソッ

Oxford University Press.

Wikipedia. Flocking behavior. Retrieved 12 2009, from Wikipedia.org:

BrokenSegue. Lamp Flow Chart.

http://en.wikipedia.org/wiki/Flocking_（behavior）

いるかもしれないが，Web サービスと連携して利用する形式

ドの入出力仕様（3c, 4s）は，インタフェース（Interface）でCaridff University. The Bees Algorithm. Retrieved 12 2009, from Bee

Wikipedia. Gerardo_Beni. Retrieved 12 2009, from Wikipedia.org: http://の根底には，RMI（または，RPC）がある。

定義する。これらの関係は，図 1 のように表せる。

Algorithm: http://www.bees-algorithm.com/modules/1/0.php

en.wikipedia.org/wiki/Gerardo_Beni

Coool. Why we need lots of optimization algorithms. Retrieved 12 2009,

Wikipedia. list of optimization algorithms. Retrieved 12 2009, fromるメソッド（または，関数）を，あたかも手元にあるかのようfrom coool.mines.edu: http://coool.mines.edu/report/node3.html

Wikipedia.org: http://en.wikipedia.org/wiki/List_of_optimization_algorith

Dorigo, M., & Di Caro, G.（1999）. 2, pp. 1470-1477. Washington DC:

msに起動して，結果の値を受け取るものである。クライアントは，IEEE.

Wikipedia. Swarm. Retrieved 12 2009, from Wikipedia.org: http://

Dorigo, M., Di Caro, G., & Gambardella, L. M.（1999）. Ant algorithms foren.wikipedia.org/wiki/Swarm

で，自分のサイトに新しい機能を構築していくことができる。

discrete optimization. Artificial Life , 5（2）, 137-172.

Wikipedia. Traveling Salesman Problem. Retrieved 12 2009, from

単純な手順であり，現在の Web サービスは，殆ど，この形にEberhart, R. C., Shi, Y., & Kennedy, J.（2001）. Swarm Intelligence. Morgan

Wikipedia.org: http://en.wikipedia.org/wiki/Travelling_salesman_problem

Kaufmann.

Yap, B. V. Termite Cathedral. http://commons.wikimedia.org/wiki/File:

基づいて利用されている。

Fir0002/Flagstaffotos. Bee Swarm On Fallen Tree. http://commons.

Termite_Cathedral_DSC03570.jpg.

wikimedia.org/wiki/File:Bee_swarm_on_fallen_tree02.jpg.

RMI は，使用側（クライアント）が，遠隔のサーバ上にあ受け取った結果によって，別の Web サービスを呼び出すこと

JVM of Client
remotelocal

objectobject（1）RMI を双方向にして，サーバが処理の途中で，クライ

remote object
remote interface

remote interface

本論文では，それを次の二つに適用して，新しい Web サー
ビスの開発の可能性を探ることを目指している。

JVM of Server

2. Reference 参照

Registry

1. Registration 登録

図1 名前の登録と参照 Fig.1 Name Registration and Reference

アント側にあるメソッドを呼び出すことを可能にする。
（RMI2）David Bradford Jr.

received his B.S. in Fine Arts from Auburn University, AL
in 1992 and his M.S. degree from Southern Polytechnic
State University in Computer Science in 2010. He is currently working on Business Intelligence projects in software applications. His research interests are swarm intelligence, pattern recognition, and image processing.

Chih-Cheng Hung

received his B.S. in business mathematics from Soochow University in Taiwan, and his M.S.
and Ph.D. in Computer Science from the University of Alabama in Huntsville, AL in 1986 and1990, respectively. He is a professor of Computer Science at Southern Polytechnic State
University, GA. He developed image processing software tools with the Department of Image Processing Applications at Intergraph Corporation from 1990 to 1993 and an associate（2）RMI の機構を利用して，オブジェクトが複数のサイト

クライアントとサーバのつながりは，メソッドを含むクラス

を巡回移動して，それぞれのサイトで，あらかじめ用意したメ（class）から，rmic（RMI compiler）によって生成したスタブ

ソッドを実行する。たとえば，サイトのファイルを読みだして，（Stub）を介して行われる。

データを収集する。これは，以前には「移動エージェント」とprofessor at Alabama A&M University from 1993 to 1999. He served as the program co-呼ばれていたものである（たとえば，文献［1］）。ここでは，chair for the Association of Computing Machinery (ACM) Symposium on Applied Computing

その動きを「巡回オブジェクト」として実現した例を取り上げ(SAC 2010) – Sierre, Switzerland from March 22 to 26, 2010. He is an associate editor ofInformation: An Intenational Interdisciplinary Journal. His research interests include image

る。それにより，複数のサイトでサービスを連続して提供する■ 2.2 RMIの例 ■上述した手順を示す例として，次の仕様を持つメソッドの呼び出しを取り上げる。

processing, pattern recognition, neural networks, genetic algorithms, artificial intelligenceand software metrics.

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