automatic kernel clustering with bee colony optimization algorithm cluster analysis is important in data mining especially if there is unsupervised data recently many clustering methods have been proposed unfortunately most of these require the definition of the number of clusters in advance this study addresses this weakness by proposing a new automatic clustering algorithm automatic kernel clustering with bee colony optimization akc bco akc bco determines the appropriate number of clusters and assigns data points to correct clusters this is accomplished by the kernel function which increases clustering capability this method is validated using several benchmark data sets the result is compared with several existing automatic clustering methods the experiment results demonstrate that the proposed akc bco is more stable and accurate than others furthermore the proposed method is also applied to a real world medical problem

a deterministic almost tight distributed algorithm for approximating single source shortest paths we present a deterministic 1 o 1 approximation n 1 2 o 1 d 1 o 1 time algorithm for solving the single source shortest paths problem on distributed weighted networks the sf congest

a discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops obtaining an optimal solution for a permutation flowshop scheduling problem with the total flowtime criterion in a reasonable computational timeframe using traditional approaches and optimization tools has been a challenge this paper presents a discrete artificial bee colony algorithm hybridized with a variant of iterated greedy algorithms to find the permutation that gives the smallest total flowtime iterated greedy algorithms are comprised of local search procedures based on insertion and swap neighborhood structures in the same context we also consider a discrete differential evolution algorithm from our previous work the performance of the proposed algorithms is tested on the well known benchmark suite of taillard the highly effective performance of the discrete artificial bee colony and hybrid differential evolution algorithms is compared against the best performing algorithms from the existing literature in terms of both solution quality and cpu times ultimately 44 out of the 90 best known solutions provided very recently by the best performing estimation of distribution and genetic local search algorithms are further improved by the proposed algorithms with short term searches the solutions known to be the best to date are reported for the benchmark suite of taillard with long term searches as well

optimizing network attacks by artificial bee colony abstract over the past few years the task of conceiving effective attacks to complex networks has arisen as an optimization problem attacks are modelled as the process of removing a number k of vertices from the graph that represents the network and the goal is to maximise or minimise the value of a predefined metric over the graph in this work we present an optimization problem that concerns the selection of nodes to be removed to minimise the maximum betweenness centrality value of the residual graph this metric evaluates the participation of the nodes in the communications through the shortest paths of the network to address the problem we propose an artificial bee colony algorithm which is a swarm intelligence approach inspired in the foraging behaviour of honeybees in this framework bees produce new candidate solutions for the problem by exploring the vicinity of previous ones called food sources the proposed method exploits useful problem knowledge in this neighbourhood exploration by considering the partial destruction and heuristic reconstruction of selected solutions the performance of the method with respect to other models from the literature that can be adapted to face this problem such as sequential centrality based attacks module based attacks a genetic algorithm a simulated annealing approach and a variable neighbourhood search is empirically shown

necessary stability conditions for delay systems with multiple pointwise and distributed delays necessary stability conditions for linear systems with multiple pointwise and distributed delays are presented new properties that connect the lyapunov delay matrix and the fundamental matrix of these systems are instrumental in proving that these conditions depend exclusively on the lyapunov delay matrix the result is illustrated by some examples

decremental single source shortest paths on undirected graphs in near linear total update time in the decremental single source shortest paths sssp problem we want to maintain the distances between a given source node s and every other node in an n node m edge graph g undergoing edge deletions while its static counterpart can be solved in near linear time this decremental problem is much more challenging even in the undirected unweighted case in this case the classic o mn total update time of even and shiloach 16 has been the fastest known algorithm for three decades at the cost of a 1 e approximation factor the running time was recently improved to n2 o 1 by bernstein and roditty 9 in this article we bring the running time down to near linear we give a 1 e approximation algorithm with m1 o 1 expected total update time thus obtaining near linear time moreover we obtain m1 o 1 log w time for the weighted case where the edge weights are integers from 1 to w the only prior work on weighted graphs in o mn time is the mn0 9 o 1 time algorithm by henzinger et al 18 19 which works for directed graphs with quasi polynomial edge weights the expected running time bound of our algorithm holds against an oblivious adversary in contrast to the previous results which rely on maintaining a sparse emulator our algorithm relies on maintaining a so called sparse h e hop set introduced by cohen 12 in the pram literature an h e hop set of a graph ge v e is a set f of weighted edges such that the distance between any pair of nodes in g can be 1 e approximated by their h hop distance given by a path containing at most h edges on g e v e f our algorithm can maintain an no 1 e hop set of near linear size in near linear time under edge deletions it is the first of its kind to the best of our knowledge to maintain approximate distances using this hop set we extend the monotone even shiloach tree of henzinger et al 20 and combine it with the bounded hop sssp technique of bernstein 4 5 and m dry 27 these two new tools might be of independent interest

critical frequencies and parameters for linear delay systems a lyapunov matrix approach abstract a new method based on recent results concerning the construction of the lyapunov matrix of delay systems reveals conditions under which the characteristic function has a root s 0 such that s 0 is also a root as the pure imaginary roots which are crucial in the stability analysis of time delay systems are of that type this method gives rise to a novel approach for the delay independent and delay dependent stability analyses of systems with delay that are multiple of a basic delay distributed delays and of a class of neutral type time delay systems a number of examples are given to illustrate the approach and to show its strength

polylog time and near linear work approximation scheme for undirected shortest paths shortest paths computations constitute one of the most fundamental network problems nonetheless known parallel shortest paths algorithms are generally inefficient they perform significantly more work product of time and processors than their sequential counterparts this gap known in the literature as the transitive closure bottleneck poses a long standing open problem our main result is an o mn e 0 s m n1 e 0 work polylog time randomized algorithm that computes paths within 1 o 1 polylog n of shortest from s source nodes to all other nodes in weighted undirected networks with n nodes and m edges for any fixed e 0 0 this work bound nearly matches the o d sm sequential time in contrast previous polylog time algorithms required nearly min o d n3 o d m2 work even when s 1 and previous near linear work algorithms required near o n time we also present faster sequential algorithms that provide good approximate distances only between distant vertices we obtain an o m sn n e 0 time algorithm that computes paths of weight 1 o 1 polylog n dist o w max polylog n where dist is the corresponding distance and w max is the maximum edge weight our chief instrument which is of independent interest are efficient constructions of sparse hop sets a d e hop set of a network g v e is a set e of new weighted edges such that mimimum weight d edge paths in v e e have weight within 1 e of the respective distances in g we construct hop sets of size o n1 e 0 where e o 1 polylog n and d o polylog n

work preserving speed up of parallel matrix computations brent s scheduling principle provides a general simulation scheme when fewer processors are available than specified by the fastest parallel algorithm such a scheme preserves under slow down the actual number of executed operations also called work in this paper we take the complementary viewpoint and rather than consider the work preserving slow down of some fast parallel algorithm we investigate the problem of the achievable speed ups of computation while preserving the work of the best known sequential algorithm for the same problem the proposed technique eminently applicable to problems of matrix computational flavor achieves its result through the interplay of two algorithms with significantly different features analogous but structurally different interplays have been used previously to improve the algorithmic efficiency of graph computations selection and list ranking we demonstrate the efficacy of our technique for the computation of path algebras in graphs and digraphs and various fundamental computations in linear algebra some of the fundamental new algorithms may have practical value for instance we substantially improve the algorithmic performance of the parallel solution of triangular and toeplitz linear systems of equations and the computation of the transitive closure of digraphs

analytic solution of a delay differential equation arising in cost functionals for systems with distributed delays the solvability of a delay differential equation arising in the construction of quadratic cost functionals i e lyapunov functionals for a linear time delay system with a constant and a distributed delay is investigated we present a delay free auxiliary ordinary differential equation system with algebraically coupled split boundary conditions which characterizes the solutions of the delay differential equation and is used for solution synthesis a spectral property of the time delay system yields a necessary and sufficient condition for existence and uniqueness of solutions to the auxiliary system equivalently the delay differential equation the result is a tractable analytic solution framework to the delay differential equation

distributed exact shortest paths in sublinear time the distributed single source shortest paths problem is one of the most fundamental and central problems in the message passing distributed computing classical bellman ford algorithm solves it in o n time where n is the number of vertices in the input graph g peleg and rubinovich 49 showed a lower bound of d s n for this problem where d is the hop diameter of g whether or not this problem can be solved in o n time when d is relatively small is a major open question despite intensive research 10 17 33 41 45 that yielded near optimal algorithms for the approximate variant of this problem no progress was reported for the original problem in this article we answer this question in the affirmative we devise an algorithm that requires o n log n 5 6 time for d e o s n log n and o d1 3 n log n 2 3 time for larger d this running time is sublinear in n in almost the entire range of parameters specifically for d e o n log2 n we also generalize our result in two directions one is when edges have bandwidth b 1 and the other is the s sources shortest paths problem for both problems our algorithm provides bounds that improve upon the previous state of the art in almost the entire range of parameters in particular we provide an all pairs shortest paths algorithm that requires o n5 3 log 2 3 n time even for b e 1 for all values of d we also devise the first algorithm with non trivial complexity guarantees for computing exact shortest paths in the multipass semi streaming model of computation from the technical viewpoint our distributed algorithm computes a hopset g of a skeleton graph g of g without first computing g itself we then conduct a bellman ford exploration in g g while computing the required edges of g on the fly as a result our distributed algorithm computes exactly those edges of g that it really needs rather than computing approximately the entire g

lyapunov matrices for time delay systems the construction of complete type lyapunov krasovki i functionals for a linear time invariant delay system depends on so called delay lyapunov matrices which satisfy a matrix delay equation with additional boundary conditions we study existence and uniqueness issues for these delay lyapunov matrices 2006 elsevier b v all rights reserved

an artificial bee colony algorithm for the maximally diverse grouping problem in this paper an artificial bee colony algorithm is proposed to solve the maximally diverse grouping problem this complex optimisation problem consists of forming maximally diverse groups with restricted sizes from a given set of elements the artificial bee colony algorithm is a new swarm intelligence technique based on the intelligent foraging behaviour of honeybees the behaviour of this algorithm is determined by two search strategies an initialisation scheme employed to construct initial solutions and a method for generating neighbouring solutions more specifically the proposed approach employs a greedy constructive method to accomplish the initialisation task and also employs different neighbourhood operators inspired by the iterated greedy algorithm in addition it incorporates an improvement procedure to enhance the intensification capability through an analysis of the experimental results the highly effective performance of the proposed algorithm is shown in comparison to the current state of the art algorithms which address the problem

all pairs shortest paths using bridging sets and rectangular matrix multiplication we present two new algorithms for solving the all pairs shortest paths apsp problem for weighted directed graphs both algorithms use fast matrix multiplication algorithms the first algorithm solves the apsp problem for weighted directed graphs in which the edge weights are integers of small absolute value in o n2 time where satisfies the equation 1 1 1 2 and 1 1 is the exponent of the multiplication of an n n matrix by an n n matrix currently the best available bounds on 1 1 obtained by coppersmith imply that 0 is an error parameter and w is the largest edge weight in the graph after the edge weights are scaled so that the smallest non zero edge weight in the graph is 1 it returns estimates of all the distances in the graph with a stretch of at most 1 e corresponding paths can also be found efficiently

a variable iterated greedy algorithm for the traveling salesman problem with time windows this paper presents a variable iterated greedy algorithm for solving the traveling salesman problem with time windows tsptw to identify a tour minimizing the total travel cost or the makespan separately the tsptw has several practical applications in both production scheduling and logistic operations the proposed algorithm basically relies on a greedy algorithm generating an increasing number of neighboring solutions through the use of the idea of neighborhood change in variable neighborhood search vns algorithms in other words neighboring solutions are generated by destructing a solution component and re constructing the solution again with variable destruction sizes in addition the proposed algorithm is hybridized with a vns algorithm employing backward and forward 1 opt local searches to further enhance the solution quality the performance of the proposed algorithm was tested on several benchmark suites from the literature experimental results confirm that the proposed algorithm is either competitive to or even better than the best performing algorithms from the literature ultimately new best known solutions are obtained for 38 out of 125 problem instances of the recently proposed benchmark suite whereas 15 out of 31 problem instances are also further improved for the makespan criterion

a simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem over the last decade many metaheuristics have been applied to the flowshop scheduling problem ranging from simulated annealing or tabu search to complex hybrid techniques some of these methods provide excellent effectiveness and efficiency at the expense of being utterly complicated in fact several published methods require substantial implementation efforts exploit problem specific speed up techniques that cannot be applied to slight variations of the original problem and often re implementations of these methods by other researchers produce results that are quite different from the original ones in this work we present a new iterated greedy algorithm that applies two phases iteratively named destruction were some jobs are eliminated from the incumbent solution and construction where the eliminated jobs are reinserted into the sequence using the well known neh construction heuristic optionally a local search can be applied after the construction phase our iterated greedy algorithm is both very simple to implement and as shown by experimental results highly effective when compared to state of the art methods

local search methods for the flowshop scheduling problem with flowtime minimization abstract flowshop scheduling is a very active research area this problem still attracts a considerable amount of interest despite the sheer amount of available results total flowtime minimization of a flowshop has been actively studied and many effective algorithms have been proposed in the last few years new best solutions have been found for common benchmarks at a rapid pace however these improvements many times come at the cost of sophisticated algorithms complex methods hinder potential applications and are difficult to extend to small problem variations replicability of results is also a challenge in this paper we examine simple and easy to implement methods that at the same time result in state of the art performance the first two proposed methods are based on the well known iterated local search ils and iterated greedy ig frameworks which have been applied with great success to other flowshop problems additionally we present extensions of these methods that work over populations something that we refer to as population based ils pils and population based ig piga respectively we calibrate the presented algorithms by means of the design of experiments doe approach extensive comparative evaluations are carried out against the most recent techniques for the considered problem in the literature the results of a comprehensive computational and statistical analysis show that the presented algorithms are very effective furthermore we show that despite their simplicity the presented methods are able to improve 12 out of 120 best known solutions of taillard s flowshop benchmark with total flowtime criterion

near linear time construction of sparse neighborhood covers this paper introduces a near linear time sequential algorithm for constructing a sparse neighborhood cover this implies analogous improvements from quadratic to near linear time for any problem whose solution relies on network decompositions including small edge cuts in planar graphs approximate shortest paths and weight and distance preserving graph spanners in particular an o log n approximation to the k shortest paths problem on an n vertex e edge graph is obtained that runs in soh n e k time

lyapunov matrices for a class of time delay systems a class of distributed time delay systems is presented for which the problem of computation of lyapunov matrices is reduced to computation of solutions of an auxiliary two point boundary problem for a special delay free system of matrix equations

hybrid genetic algorithm for permutation flowshop scheduling problems with total flowtime minimization in this paper a hga hybrid genetic algorithm is proposed for permutation flowshop scheduling problems pfsp with total flowtime minimization which are known to be np hard one of the chromosomes in the initial population is constructed by a suitable heuristic and the others are yielded randomly an artificial chromosome is generated by a weighted simple mining gene structure with which a new crossover operator is presented additionally two effective heuristics are adopted as local search to improve all generated chromosomes in each generation the hga is compared with one of the most effective heuristics and a recent meta heuristic on 120 benchmark instances experimental results show that the hga outperforms the other two algorithms for all cases furthermore hga obtains 115 best solutions for the benchmark instances 92 of which are newly discovered

high probability parallel transitive closure algorithms there is a straightforward algorithm for computing the transitive closure of an n node graph in o log 2 n time on an erew pram using n 3 log n processors or indeed with m n log n processors if serial matrix multiplication in m n time can be done this algorithm is within a log factor of optimal in work processor time product for solving the all pairs transitive closure problem for dense graphs however this algorithm is far from optimal when either a the graph is sparse or b we want to solve the single source transitive closure problem it would be ideal to have an mathcal nc algorithm for transitive closure that took about e processors for the single source problem on a graph with n nodes and e geqq n arcs or about en processors for the all pairs problem on the same graph while an algorithm that good cannot be offered algorithms with the following performance can be offered 1 for single source tilde o n varepsilon time with tilde o en 1 2 varepsil

a hybrid genetic local search algorithm for the permutation flowshop scheduling problem traditionally the permutation flowshop scheduling problem pfsp was with the criterion of minimizing makespan the permutation flowshop scheduling problem to minimize the total flowtime has attracted more attention from researchers in recent years in this paper a hybrid genetic local search algorithm is proposed to solve this problem with each of both criteria the proposed algorithm hybridizes the genetic algorithm and a novel local search scheme that combines two local search methods the insertion search is and the insertion search with cut and repair iscr it employs the genetic algorithm to do the global search and two local search methods to do the local search two local search methods play different roles in the search process the insertion search is responsible for searching a small neighborhood while the insertion search with cut and repair is responsible for searching a large neighborhood furthermore the orthogonal array based crossover operator is designed to enhance the ga s capability of intensification the experimental results show the advantage of combining the two local search methods the performance of the proposed hybrid genetic algorithm is very competitive for the pfsp with the total flowtime criterion it improved 66 out of the 90 current best solutions reported in the literature in short term search and it also improved all the 20 current best solutions reported in the literature in long term search for the pfsp with the makespan criterion the proposed algorithm also outperforms the other three methods recently reported in the literature

lyapunov matrices for a class of time delay systems with piecewise constant kernel abstractin this contribution we study time delay systems with distributed delay whose kernel is piecewise constant it is shown that the problem of finding a lyapunov matrix for this class of time delay systems can be reduced to the computation of solutions to an auxiliary system of linear differential equations with boundary conditions the problem of solving the auxiliary system is then reduced to the solution of a system of linear algebraic equations it is established that the auxiliary system with boundary conditions admits a unique solution if and only if there exists a unique lyapunov matrix

a particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem abstract in this paper a particle swarm optimization algorithm pso is presented to solve the permutation flowshop sequencing problem pfsp with the objectives of minimizing makespan and the total flowtime of jobs for this purpose a heuristic rule called the smallest position value spv borrowed from the random key representation of bean j c bean genetic algorithm and random keys for sequencing and optimization orsa journal of computing 6 2 1994 154 160 was developed to enable the continuous particle swarm optimization algorithm to be applied to all classes of sequencing problems in addition a very efficient local search called variable neighborhood search vns was embedded in the pso algorithm to solve the well known benchmark suites in the literature the pso algorithm was applied to both the 90 benchmark instances provided by taillard e taillard benchmarks for basic scheduling problems european journal of operational research 64 1993 278 285 and the 14 000 random narrow random and structured benchmark instances provided by watson et al j p watson l barbulescu l d whitley a e howe contrasting structured and random permutation flowshop scheduling problems search space topology and algorithm performance orsa journal of computing 14 2 2002 98 123 for makespan criterion the solution quality was evaluated according to the best known solutions provided either by taillard or watson et al the total flowtime criterion was evaluated with the best known solutions provided by liu and reeves j liu c r reeves constructive and composite heuristic solutions to the p ci scheduling problem european journal of operational research 132 2001 439 452 and rajendran and ziegler c rajendran h ziegler ant colony algorithms for permutation flowshop scheduling to minimize makespan total flowtime of jobs european journal of operational research 155 2 2004 426 438 for the total flowtime criterion 57 out of the 90 best known solutions reported by liu and reeves and rajendran and ziegler were improved whereas for the makespan criterion 195 out of the 800 best known solutions for the random and narrow random problems reported by watson et al were improved by the vns version of the pso algorithm

a quantum behaved particle swarm optimization with memetic algorithm and memory for continuous non linear large scale problems abstract quantum behaved particle swarm optimization qpso a novel variant of pso inspired by quantum mechanics is a global convergence guaranteed algorithm which outperforms the original pso in search ability and has fewer parameters to control but as many other psos it is easy to fall into local optimum in solving high dimensional complex optimization problems this paper proposes an improved qpso algorithm for continuous non linear large scale problems based on memetic algorithm and memory mechanism the memetic algorithm is used to make all particles each particle corresponding to a memetic gain some experience through a local search before being involved in the evolutionary process and the memory mechanism is used to introduce a so called bird kingdom with memory capacity both of which can improve the global search ability of the algorithm another difference compared to the previous qpsos is that we let each dimension of a particle update with the same random number thus increasing the speed of convergence and enhancing the ability of local search numerical experiments are conducted to compare the proposed algorithm with different variants of pso and other swarm intelligence algorithms the experimental results show the superiority of the proposed approach on benchmark test functions

adapting parallel algorithms to the w stream model with applications to graph problems in this paper we show how parallel algorithms can be turned into efficient streaming algorithms for several classical combinatorial problems in the w stream model in this model at each pass one input stream is read one output stream is written and data items have to be processed using limited space streams are pipelined in such a way that the output stream produced at pass i is given as input stream at pass i 1 we first introduce a simulation technique that allows turning efficient pram algorithms into optimal w stream ones for many classical combinatorial problems including list ranking and euler tour of a tree for other problems most notably graph problems however this technique leads to suboptimal algorithms to overcome this difficulty we introduce the relaxedpram rpram computational model as an intermediate model between pram and w stream rpram allows every processor to access a non constant number of memory cells per parallel round albeit with some restrictions the rpram model while being more powerful than the pram model can be simulated in w stream within the same asymptotic bounds the extra power provided by rpram allows us in many cases to substantially reduce the number of processors while maintaining the same number of parallel rounds leading to more efficient w stream simulations of parallel algorithms our rpram technique gives new insights on developing streaming algorithms and yields efficient algorithms for several classical problems in this model including sorting connectivity minimum spanning tree biconnected components and maximal independent set in addition to allowing smooth space passes tradeoffs our algorithms are also shown by proving almost tight communication complexity based lower bounds in w stream to be optimal up to polylog factors