# Flower Pollination Algorithm with Dimension by Dimension Improvement

Authors: Rui Wang; Yongquan Zhou
Journal: Mathematical Problems in Engineering
 (2014)

---

## Abstract

Flower pollination algorithm (FPA) is a new nature-inspired intelligent algorithm which uses the whole update and evaluation strategy on solutions. For solving multidimension function optimization problems, this strategy may deteriorate the convergence speed and the quality of solution of algorithm due to interference phenomena among dimensions. To overcome this shortage, in this paper a dimension by dimension improvement based flower pollination algorithm is proposed. In the progress of iteration of improved algorithm, a dimension by dimension based update and evaluation strategy on solutions is used. And, in order to enhance the local searching ability, local neighborhood search strategy is also applied in this improved algorithm. The simulation experiments show that the proposed strategies can improve the convergence speed and the quality of solutions effectively.


---

## Body

## 1. Introduction

In recent years, more and more bioinspired algorithms are proposed, such as genetic algorithm (GA) [1], simulated annealing (SA) [2], particle swarm optimization (PSO) [3], firefly algorithm (FA) [4], glowworm swarm optimization (GSO) [5], monkey search (MS) [6], bacterial foraging optimization algorithm (BFOA) [7], invasive weed optimization (IWO) [8], cultural algorithms (CA) [9], and harmony search (HS) [10]. Because of their advantages of global and parallel efficiency, robustness, and universality, swarm intelligence algorithms have been widely used in engineering optimization, scientific computing, automatic control, and other fields.Flower pollination algorithm (proposed by Yang in 2012) [11] is a new population-based intelligent optimization algorithm by simulating flower pollination behavior. And FPA has been extensively researched to solve Integer Programming Problems [12], Sudoku Puzzles [13], and Wireless Sensor Network Lifetime Global Optimization [14] in the last two years by scholars. It is estimated that there are over 250,000 types of flowering plants in nature. And researchers of biology considered that almost four-fifths of all plant species are flowering species. Flower pollination behavior stems from the purpose of reproduction. From the biological evolution point of view, the objective of flower pollination is the survival of the fittest and the optimal reproduction of species. All these factors and processes of flower pollination interact so as to achieve optimal reproduction of the flowering plants. In nature, pollination can be divided into two parts: abiotic and biotic. Almost 90% pollen grains are transferred by insects and animals; we call this biotic pollination. The other 10% pollen grains are transferred by wind [15, 16]. They do not need pollinators. And we call this form abiotic pollination. Pollinators can be very diverse; researches show almost 200,000 kinds of pollinators.Self-pollination and cross-pollination are two different ways of pollination [17]. Cross-pollination means pollination can occur from pollen of a flower of a different plant, and self-pollination is just the opposite. Biotic, cross-pollination can occur at long distance; the pollinators such as bees, bats, birds can fly a long distance; thus they can be considered as the global pollinators. And these pollinators can fly as Lévy flight behavior [18], with fly distance steps obeying a Lévy distribution. Thus, this can inspire to design new optimization algorithm. Flower pollination algorithm is an optimization algorithm which simulates the flower pollination behavior mentioned above; flower pollination algorithm can also be divided into global pollination process and local pollination process.


## 2. FPA with Dimension by Dimension Improvement

In order to enhance the global searching and local searching abilities, we applied three optimization strategies to basic flower pollination algorithm (FPA); those were local neighborhood searching strategy (LNSS) [19], dimension by dimension evaluation and improvement strategy (DDEIS), and dynamic switching probability strategy (DSPS).


### 2.1. Local Neighborhood Search Strategy (LNSS)

FPA (developed by Yang and Deb) uses differential evolution (DE) algorithm [20] to do local search. And experiment results show that the local search ability of DE is limited. Thus, we add LNSS to local search process to enhance its exploitation ability.Firstly, we should explain a model (local neighborhood model). In this model, each vector uses the best vector of only a small neighborhood rather than the entire population to do the mutation. We suppose that there exists a differential evolutionary populationPG=[X1,G,X2,G,…,Xi+1,G], and each Xi,G(i=1,2,3,…,NP) is a parameter vector, and its dimension is D. Each vector subscript index is randomly divided to ensure the diversity of each neighborhood. For each vector Xi,G, we can define a neighborhood, and the radius is k(2k+1<NP). The neighborhood consists of vector Xi-k,G,…,Xi,G,…,Xi+k,G. Assume that the vectors accord the subscript indices in a ring topology structure. We can take XNP,G and X2,G as two direct neighbors of X1,G. The concept of local neighborhood model is shown in Figures 1 and 2. The neighborhood topology here is static and determined by the collection of vector subscript indices. And the local neighborhood model can be expressed in the following formula:
(1)Xi,G+1=Xi,G+α(Xn_besti,G-Xi,G)+β(Xp,G-Xq,G),
where Xn_besti,G is the best vector of Xi,G neighborhood, p,q∈[i-k,i+k](p≠q≠i), and α,β are two scale factors.Figure 1
Neighborhood ring topology.Figure 2
ANOVA tests forf01.


### 2.2. Dimension by Dimension Evaluation and Improvement Strategy (DDEIS)

Flower pollination algorithm uses the whole update and evaluation strategy on solutions. For solving multidimensional function optimization problems, this strategy may deteriorate the convergence speed and the quality of solution due to interference phenomena among dimensions. To overcome this shortage, we add this strategy to FPA in local search process.In FPA, Lévy flight can improve the diversity of population and strengthen the global search ability of the algorithm. But for multidimensional objective function, overall update evaluation strategy will affect the convergence rate and quality of solutions. DDEIS updates dimension by dimension. Assume that objective function isf(X)=X12+X22+X32 and Xg,i=(0.5,0.5,0.5) is a solution tof(X).The objective valuef(Xg,i)=0.75. We use formula (1) to update Xg,i and get Xg+1,i=(0,1,-1). For example, when first dimension value of Xg,i updates from 0.5 to 0, combined with the value of other dimensions, we can get a new Xg+1,i=(0,0.5,0.5). The objective value f(Xg+1,i)=0.5<f(Xg,i); it can improve current solution. Thus, we accept this update and update operation into the next dimension. If first dimension value of Xg,i updates from 0.5 to 1, we can get a new Xg+1,i=(1,0.5,0.5). The objective value f(Xg+1,i)=1.5>f(Xg,i), it fails to improve Xg,i, and we should abandon the current dimension updated value and update operation into the next dimension. The strategy is described in Algorithm 1.Algorithm 1:Dimension by dimension evaluation and improvement strategy.
temp2 =Xg,i;
temp =Xg+1,i;
for  m=1:d
temp3 = temp2;
temp2(m) = temp(m);
if fitness(temp2 ) > fitness(temp3),
temp2(m) = Xg,i(m);
endif
endfor


### 2.3. Dynamic Switching Probability Strategy (DSPS)

In FPA, local search and global search are controlled by a switching probabilityp∈[0,1], and it is a constant value. We suppose that a reasonable algorithm should do more global search at the beginning of searching process and global search should be less in the end. Thus, we applied the dynamic switching probability strategy (DSPS) to adjust the proportion of two kinds of searching process. Switching probability p can alter according to the following formula:
(2)p=0.6-0.1×(Max⁡_iter-t)Max⁡_iter,
where Max⁡_iter is the maximum iterations of the DDIFPA and t is current iteration. Specific implementation steps of FPA with dimension by dimension improvement (DDIFPA) can be summarized in the pseudocode shown in Algorithm 2.Algorithm 2:FPA with dimension by dimension improvement (DDIFPA).
Objective min or maxf(x),x=(x1,x2,…,xd)
Initialize a population ofn flowers/pollen gametes with random solutions
Find the best solutiong* in the initial population
while (t<MaxGeneration)
for  i=1:n (all n flowers in the population)
Get p according to formula (2);
if rand < p
Draw a (d-dimensional) step vector L which obeys a Lévy distribution
Global pollination viaxit+1=xit+γL(λ)(g*-xit);
else
Drawε from a uniform distribution in [0,1];
Local pollination viaXi,G+1=Xi,G+α(Xn_besti,G-Xi,G)+β(Xp,G-Xq,G);
whereα=β=ε;
end if
Evaluate new solutions via DDEIS
If new solutions are better, update them in the population
end for
find the current best solutiong*
end while


## 2.1. Local Neighborhood Search Strategy (LNSS)

FPA (developed by Yang and Deb) uses differential evolution (DE) algorithm [20] to do local search. And experiment results show that the local search ability of DE is limited. Thus, we add LNSS to local search process to enhance its exploitation ability.Firstly, we should explain a model (local neighborhood model). In this model, each vector uses the best vector of only a small neighborhood rather than the entire population to do the mutation. We suppose that there exists a differential evolutionary populationPG=[X1,G,X2,G,…,Xi+1,G], and each Xi,G(i=1,2,3,…,NP) is a parameter vector, and its dimension is D. Each vector subscript index is randomly divided to ensure the diversity of each neighborhood. For each vector Xi,G, we can define a neighborhood, and the radius is k(2k+1<NP). The neighborhood consists of vector Xi-k,G,…,Xi,G,…,Xi+k,G. Assume that the vectors accord the subscript indices in a ring topology structure. We can take XNP,G and X2,G as two direct neighbors of X1,G. The concept of local neighborhood model is shown in Figures 1 and 2. The neighborhood topology here is static and determined by the collection of vector subscript indices. And the local neighborhood model can be expressed in the following formula:
(1)Xi,G+1=Xi,G+α(Xn_besti,G-Xi,G)+β(Xp,G-Xq,G),
where Xn_besti,G is the best vector of Xi,G neighborhood, p,q∈[i-k,i+k](p≠q≠i), and α,β are two scale factors.Figure 1
Neighborhood ring topology.Figure 2
ANOVA tests forf01.


## 2.2. Dimension by Dimension Evaluation and Improvement Strategy (DDEIS)

Flower pollination algorithm uses the whole update and evaluation strategy on solutions. For solving multidimensional function optimization problems, this strategy may deteriorate the convergence speed and the quality of solution due to interference phenomena among dimensions. To overcome this shortage, we add this strategy to FPA in local search process.In FPA, Lévy flight can improve the diversity of population and strengthen the global search ability of the algorithm. But for multidimensional objective function, overall update evaluation strategy will affect the convergence rate and quality of solutions. DDEIS updates dimension by dimension. Assume that objective function isf(X)=X12+X22+X32 and Xg,i=(0.5,0.5,0.5) is a solution tof(X).The objective valuef(Xg,i)=0.75. We use formula (1) to update Xg,i and get Xg+1,i=(0,1,-1). For example, when first dimension value of Xg,i updates from 0.5 to 0, combined with the value of other dimensions, we can get a new Xg+1,i=(0,0.5,0.5). The objective value f(Xg+1,i)=0.5<f(Xg,i); it can improve current solution. Thus, we accept this update and update operation into the next dimension. If first dimension value of Xg,i updates from 0.5 to 1, we can get a new Xg+1,i=(1,0.5,0.5). The objective value f(Xg+1,i)=1.5>f(Xg,i), it fails to improve Xg,i, and we should abandon the current dimension updated value and update operation into the next dimension. The strategy is described in Algorithm 1.Algorithm 1:Dimension by dimension evaluation and improvement strategy.
temp2 =Xg,i;
temp =Xg+1,i;
for  m=1:d
temp3 = temp2;
temp2(m) = temp(m);
if fitness(temp2 ) > fitness(temp3),
temp2(m) = Xg,i(m);
endif
endfor


## 2.3. Dynamic Switching Probability Strategy (DSPS)

In FPA, local search and global search are controlled by a switching probabilityp∈[0,1], and it is a constant value. We suppose that a reasonable algorithm should do more global search at the beginning of searching process and global search should be less in the end. Thus, we applied the dynamic switching probability strategy (DSPS) to adjust the proportion of two kinds of searching process. Switching probability p can alter according to the following formula:
(2)p=0.6-0.1×(Max⁡_iter-t)Max⁡_iter,
where Max⁡_iter is the maximum iterations of the DDIFPA and t is current iteration. Specific implementation steps of FPA with dimension by dimension improvement (DDIFPA) can be summarized in the pseudocode shown in Algorithm 2.Algorithm 2:FPA with dimension by dimension improvement (DDIFPA).
Objective min or maxf(x),x=(x1,x2,…,xd)
Initialize a population ofn flowers/pollen gametes with random solutions
Find the best solutiong* in the initial population
while (t<MaxGeneration)
for  i=1:n (all n flowers in the population)
Get p according to formula (2);
if rand < p
Draw a (d-dimensional) step vector L which obeys a Lévy distribution
Global pollination viaxit+1=xit+γL(λ)(g*-xit);
else
Drawε from a uniform distribution in [0,1];
Local pollination viaXi,G+1=Xi,G+α(Xn_besti,G-Xi,G)+β(Xp,G-Xq,G);
whereα=β=ε;
end if
Evaluate new solutions via DDEIS
If new solutions are better, update them in the population
end for
find the current best solutiong*
end while


## 3. Numerical Simulation Experiments

In this section, we applied 12 standard test functions [21] to evaluate the optimal performance of FPA with dimension by dimension improvement (DDIFPA). The mean and standard deviation results of 20 independent runs for each algorithm have been summarized in Table 2. The 12 standard benchmark functions have been widely used in the literature. The dimensions, scopes, optimal values, and iterations of 12 functions are in Table 1. We also do some high-dimensional tests, and the results are showed in Table 3.Table 1
Benchmark test functions.
Benchmark test functions
Dimension
Range
Optimum
Iterations
f
01
=
∑
i
=
1
n
x
i
2
30
[
-
100,100
]
0
1500
f
02
=
∑
i
=
1
n
|
x
i
|
+
∏
i
=
1
n
|
x
i
|
30
[
-
10,10
]
0
2000
f
03
=
ma
x
i
{
|
x
i
|
,
1
≤
i
≤
D
}
30
[
-
100,100
]
0
5000
f
04
=
∑
i
=
1
D
-
1
[
100
(
x
i
+
1
-
x
i
2
)
2
+
(
x
i
-
1
)
2
]
30
[
-
30,30
]
0
5000
f
05
=
∑
i
=
1
D
i
x
i
4
+
random
[
0,1
)
30
[
-
1.28,1.28
]
0
3000
f
06
=
∑
i
=
1
D
-
x
i
sin
⁡
⁡
(
|
x
i
|
)
30
[
-
500,500
]
−418.9829 *n
3000
f
07
=
∑
i
=
1
D
[
x
i
2
-
10
cos
⁡
(
2
π
x
i
)
+
10
]
30
[
-
5.12,5.12
]
0
3000
f
08
=
-
20
exp
⁡
(
-
0.2
1
D
∑
i
=
1
D
x
i
2
)
-
exp
⁡
(
1
D
∑
i
=
1
D
cos
⁡
2
π
x
i
)
+
20
+
e
30
[
-
32,32
]
0
1500
f
09
(
x
)
=
1
4000
∑
i
=
1
n
x
i
2
-
∏
i
=
1
n
cos
⁡
(
x
i
2
)
+
1
30
[
-
600,600
]
0
2000
f
10
(
x
)
=
π
D
{
10
sin
⁡
2
⁡
(
π
y
1
)
+
∑
i
=
1
D
-
1
(
y
-
1
)
2
[
1
+
10
sin
⁡
2
⁡
(
π
y
1
)
]
+
(
y
D
-
1
)
2
}
+
∑
i
=
1
D
u
(
x
i
,
10,100,4
)
y
i
=
1
+
x
i
+
1
4
u
(
x
i
,
a
,
k
,
m
)
=
{
k
(
x
i
-
a
)
m
,
x
i
>
a
0
,
-
a
≤
x
i
≤
a
k
(
-
x
i
-
z
)
m
,
x
i
<
a
30
[
-
50,50
]
0
1500
f
11
(
x
)
=
-
∑
i
=
1
4
c
i
exp
⁡
[
∑
j
=
1
3
a
i
j
(
x
j
-
p
i
j
)
2
]
3
[
0,1
]
−3.8628
100
f
12
(
x
)
=
-
∑
i
=
1
10
[
(
X
-
a
i
)
(
X
-
a
i
)
T
+
c
i
]
-
1
4
[
0,10
]
−10.5364
100Table 2
Experiment results of bench mark functions for different algorithms.
Functions
PSO
DE
RCBBO
CS
FA
GSA
ABC
FPA
DDIFPA
f
01
Mean
3.33
E
-
10
5.60
E
-
14
0.3737
5.66
E
-
06
1.70
E
-
03
3.37
E
-
18
2.99
E
-
20
123.5791661
4.62
E
-
289
Std.
7.04
E
-
10
4.41
E
-
14
0.1181
2.86
E
-
06
4.06
E
-
04
8.09
E
-
19
2.15
E
-
20
52.5878636
0
Rank
5
4
8
6
7
3
2
9
1
f
02
Mean
6.66
E
-
11
4.73
E
-
10
0.1656
2.00
E
-
03
4.53
E
-
02
8.92
E
-
09
1.42
E
-
15
8.27840887
3.61
E
-
196
Std.
9.26
E
-
11
1.78
E
-
10
0.0342
8.10
E
-
04
3.38
E
-
02
1.33
E
-
09
5.53
E
-
16
2.19245633
0
Rank
3
4
8
6
7
5
2
9
1
f
03
Mean
7.9997
0.2216
7.9738
3.2388
0.0554
9.93
E
-
10
18.5227
3.759984116
4.52
E
-
127
Std.
2.535
0.243
2.6633
0.6644
0.0101
1.19
E
-
10
4.2477
1.301640714
5.83
E
-
127
Rank
7
4
8
5
3
2
9
6
1
f
04
Mean
46.9202
0.2657
64.6907
8.0092
38.1248
20.0819
0.0441
32.25150881
0.065392617
Std.
38.0312
1.0293
36.2782
1.9188
30.3962
0.1722
0.0707
12.70746052
0.094325113
Rank
8
3
9
4.0000
7
5
1
6
2
f
05
Mean
0.0135
0.0042
0.003
0.0096
0.0082
0.0039
0.0324
0.029755135
0.003729368
Std.
0.0041
0.0014
0.0012
0.0028
0.0093
0.0013
0.0059
0.013355902
0.000996113
Rank
7
4
1
6
5
3
9
8
2
f
06
Mean
-
8.83
E
+
03
-
1.13
E
+
04
-
1.26
E
+
04
-
9.15
E
+
03
-
6.22
E
+
03
-
3.05
E
+
03
-
1.25
E
+
04
-
8448.868832
-
12569.48662
Std.
611.159
1.81
E
+
03
0.5758
2.53
E
+
02
7.72
E
+
02
3.39
E
+
02
61.1186
292.6519355
1.92
E
-
12
Rank
6
4
2
5
8
9
3
7
1
f
07
Mean
18.2675
134.6789
0.0385
51.2202
23.5213
7.2831
0
75.97229041
0
Std.
4.7965
28.8598
0.0154
8.1069
8.3683
1.8991
0
12.38031696
0
Rank
5
9
3
7
6
3
1
8
1
f
08
Mean
3.87
E
-
06
7.47
E
-
08
0.1947
2.375
0.0094
1.47
E
-
09
1.19
E
-
09
3.706074906
4.44
E
-
15
Std.
2.86
E
-
06
3.11
E
-
08
0.0461
1.1238
0.0014
1.44
E
-
10
5.01
E
-
10
0.519323445
0
Rank
5
4
7
8
6
2
3
9
1
f
09
Mean
0.0168
0
0.2765
4.49
E
-
05
0.0025
0.01265
0
4.559765038
0
Std.
0.0205
0
0.0796
8.96
E
-
05
4.69
E
-
04
0.0216
0
1.769274137
0
Rank
7
1
8
4
5
6
1
9
1
f
10
Mean
0.0083
4.71
E
-
15
0.002
0.5071
8.87
E
-
06
2.04
E
-
20
1.19
E
-
21
4.3551
1.57
E
-
32
Std.
0.0287
3.26
E
-
15
0.0023
0.2662
2.80
E
-
06
4.53
E
-
21
1.08
E
-
21
1.1793
2.89
E
-
48
Rank
7
4
6
8
5
3
2
9
1
f
11
Mean
-
3.8628
-
3.8628
-
3.8627
-
3.8628
-
3.8613
-
3.8625
-
3.8628
-
3.861091803
-
3.862782148
Std.
3.13
E
-
12
2.30
E
-
15
1.41
E
-
04
1.40
E
-
05
0.0037
3.88
E
-
04
1.37
E
-
10
0.00149557
1.43
E
-
13
Rank
3
1
6
5
8
7
4
9
2
f
12
Mean
-
8.9611
-
10.5364
-
9.3514
-
9.7534
-
10.2297
-
8.2651
-
10.5339
-
5.006727392
-
10.53636839
Std.
2.8381
3.97
E
-
06
2.6288
0.4913
1.5332
2.8868
0.0054
1.235737304
7.4406
E
-
05
Rank
7
1
6
5
4
8
3
9
2Table 3
High-dimensional functions test results.
Functions
Dimensions
Means
Std.
Best
Worst
f
01
1000
6.47933690759837
E
-
284
0
9.49725772050939
E
-
288
3.19181958962329
E
-
283
f
02
500
2.24079551697238
E
-
193
0
5.54622672996093
E
-
195
5.86867756931104
E
-
193
f
04
500
3.4272355502299
E
-
17
2.42530519040223
E
-
17
6.91784434902547
E
-
18
6.99285915411276
E
-
17
f
07
500
0
0
0
0
f
10
500
1.57054477178664
E
-
32
0
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### 3.1. Experimental Setup

All of the algorithm was programmed in MATLAB R2012a; numerical experiment was set up on AMD Athlont (tm)II*4640 processor and 2 GB memory.


### 3.2. Comparison of Each Algorithm Performance

The proposed DDIFPA algorithm is compared with mainstream swarm intelligence algorithms FPA [11], PSO [22], DE [23], RCBBO [24], GSA [25], FA [26], CS [27], and ABC [28], respectively, using the mean and standard deviations to compare their optimal performances. The setting values of algorithm control parameters of the mentioned algorithms are given as follows.PSO parameters setting: weight factorω=0.6, c1=c2=2. The population size is 100 [22].DE parameters setting:F=0.5 and CR = 0.9 in accordance with the suggestions given in [23]; the population size is 100.ABC parameters setting:limit = 5D has been used as recommended in [22]; the population size is 50 because this algorithm has two phases.RCBBC parameters setting: maximum immigration rate:I= 1, maximum emigration rate: E=1, andmutation probability: mmax⁡=0.005 have been used as recommended in [24]; the population size is 100.CS parameters setting:β=1.5 and ρ0=1.5 have been used as recommended in [27]; the population size is 50 because this algorithm has two phases.GSA parameters setting:G0=100,α=20andK0 which is set toNPand is decreased linearly to 1 have been used as recommended in [25]; the population size is 100.FA parameters setting:α0=0.5, β0=0.2, and γ=1 have been used as recommended in [26]; the population size is 100.FPA parameters setting: the population size is 50 because this algorithm has two phases [11];DDIFPA parameters setting: the population size is 50 because this algorithm has two phases.From the rank of each function in Table2, we can conclude that DDIFPA provides many of the best results are better than FPA and other algorithms, especially for functions f01, f03, and f05. For f01 the mean and standard deviation of DDIFPA are much higher than FPA. For f03, the mean and standard deviation of DDIFPA are 117 orders of magnitude higher than GSA and 127 orders of magnitude higher than FPA. For f04, DDIFPA and FPA fail to give the best optimal solution. For f05, the mean and standard deviation of DDIFPA are 2 orders of magnitude higher than FPA.Figures2 and 3 show the graphical analysis results of ANOVA test. As can be seen in Figure 2, when solving function f01, most of the algorithms can obtain the stable optimal value after 20 independent runs except RCBBO algorithm, and, in Figure 3, when solving the function f05, DDIFPA is more stable than other algorithms.Figure 3
ANOVA tests forf05.Figures4 and 5 show the fitness function curve evolution of each algorithm for f01 and f05. From the two figures, we can conclude that DDIFPA has a faster convergence rate and a higher optimizing precision.Figure 4
Fitness function curve evolution forf01.Figure 5
Fitness function curve evolution forf05.For multimodal functionsf06tof10 with many local minima, the final results are more important because these functions can reflect the ability of algorithm to escape from poor local optima and obtain the global optimum.As can be seen in Table2, for f06 and f07, DDIFPA are in first place; ABC achieve the optimal value when solving f07. For f08 the mean and standard deviation of DDIFPA are 15 orders of magnitude higher than FPA. For f09, ABC and DDIFPA all achieve the optimal value and the standard deviations are all 0. For f10, the mean of DDIFPA is 11 orders of magnitude higher than ABC, and the standard deviation of DDIFPA is 27 orders of magnitude higher than ABC.Figures6 and 7 show the graphical analysis results of the ANOVA tests. Figure 6 shows that RCBBO, ABC, and DDIFPA can obtain the relatively stable optimal values. Figure 7 shows that when solving function f10, most of the algorithms can obtain the stable optimal value after 20 independent runs.Figure 6
ANOVA tests forf07.Figure 7
ANOVA tests forf10.Figures8 and 9 show the fitness function curve evolution. From Figure 9, we can conclude that both ABC and DDIFPA converge to the optimal solution. From Figure 9, we can conclude that DDIFPA converges to a more precise point than other algorithms, and its convergence speed is faster.Figure 8
Fitness function curve evolution forf07.Figure 9
Fitness function curve evolution forf10.From Table2, f11 and f12 are multimodal low-dimensional functions. For f11, the solutions of most of the algorithms are accurate in 3 to 4 decimal places, and the rank of DDIFPA is second. For f12, the rank is second too, and the experiment results show that DDIFPA can do a good job in solving multimodal low-dimensional problems.


### 3.3. Experimental Analysis

We have carried out benchmark validations for unimodal and multimodal test functions using the proposed algorithm (DDIFPA) with three improvement strategies (local neighborhood search strategy, dimension by dimension evaluation and improvement strategy, and dynamic switching probability strategy). An optimization process can be divided into two key components (local search and global search); we use a dynamic switching probabilityp∈[0,1] to control the whole searching process. LNSS and DDEIS are applied to the local search process and enhance its exploitation ability. Among 12 test functions listed above, f01tof05 are unimodal, and the remarkable achievements confirm that DDIFPA have stronger exploitation ability than FPA and other algorithms. And DSPS, which could improve the ability of escape from poor local optima, was applied to enhance the exploration ability. That also balanced exploitation and exploration dynamically. For multimodal benchmark functions (f06tof12), we can conclude that DDIFPA converges to a more precise point than other algorithms, and its convergence speed is faster. Our simulation results for finding the global optima of various test functions suggest that DDIFPA can outperform the FPA and other mentioned algorithms in terms of both precision and convergence speed.


### 3.4. High-Dimensional Functions Test

In previous sections, 12 standard test functions are applied to evaluate the optimal performances of the FPA with dimension by dimension improvement (DDIFPA) in the case of low dimension. In order to evaluate the performances of DDIFPA comprehensively, we also do some high-dimensional tests inf1,f2,f4,f7,f10. The test results are shown in Table 3. As can be seen in Table 3, DDIFPA can also solve high-dimensional problems efficiently and stably.


## 3.1. Experimental Setup

All of the algorithm was programmed in MATLAB R2012a; numerical experiment was set up on AMD Athlont (tm)II*4640 processor and 2 GB memory.


## 3.2. Comparison of Each Algorithm Performance

The proposed DDIFPA algorithm is compared with mainstream swarm intelligence algorithms FPA [11], PSO [22], DE [23], RCBBO [24], GSA [25], FA [26], CS [27], and ABC [28], respectively, using the mean and standard deviations to compare their optimal performances. The setting values of algorithm control parameters of the mentioned algorithms are given as follows.PSO parameters setting: weight factorω=0.6, c1=c2=2. The population size is 100 [22].DE parameters setting:F=0.5 and CR = 0.9 in accordance with the suggestions given in [23]; the population size is 100.ABC parameters setting:limit = 5D has been used as recommended in [22]; the population size is 50 because this algorithm has two phases.RCBBC parameters setting: maximum immigration rate:I= 1, maximum emigration rate: E=1, andmutation probability: mmax⁡=0.005 have been used as recommended in [24]; the population size is 100.CS parameters setting:β=1.5 and ρ0=1.5 have been used as recommended in [27]; the population size is 50 because this algorithm has two phases.GSA parameters setting:G0=100,α=20andK0 which is set toNPand is decreased linearly to 1 have been used as recommended in [25]; the population size is 100.FA parameters setting:α0=0.5, β0=0.2, and γ=1 have been used as recommended in [26]; the population size is 100.FPA parameters setting: the population size is 50 because this algorithm has two phases [11];DDIFPA parameters setting: the population size is 50 because this algorithm has two phases.From the rank of each function in Table2, we can conclude that DDIFPA provides many of the best results are better than FPA and other algorithms, especially for functions f01, f03, and f05. For f01 the mean and standard deviation of DDIFPA are much higher than FPA. For f03, the mean and standard deviation of DDIFPA are 117 orders of magnitude higher than GSA and 127 orders of magnitude higher than FPA. For f04, DDIFPA and FPA fail to give the best optimal solution. For f05, the mean and standard deviation of DDIFPA are 2 orders of magnitude higher than FPA.Figures2 and 3 show the graphical analysis results of ANOVA test. As can be seen in Figure 2, when solving function f01, most of the algorithms can obtain the stable optimal value after 20 independent runs except RCBBO algorithm, and, in Figure 3, when solving the function f05, DDIFPA is more stable than other algorithms.Figure 3
ANOVA tests forf05.Figures4 and 5 show the fitness function curve evolution of each algorithm for f01 and f05. From the two figures, we can conclude that DDIFPA has a faster convergence rate and a higher optimizing precision.Figure 4
Fitness function curve evolution forf01.Figure 5
Fitness function curve evolution forf05.For multimodal functionsf06tof10 with many local minima, the final results are more important because these functions can reflect the ability of algorithm to escape from poor local optima and obtain the global optimum.As can be seen in Table2, for f06 and f07, DDIFPA are in first place; ABC achieve the optimal value when solving f07. For f08 the mean and standard deviation of DDIFPA are 15 orders of magnitude higher than FPA. For f09, ABC and DDIFPA all achieve the optimal value and the standard deviations are all 0. For f10, the mean of DDIFPA is 11 orders of magnitude higher than ABC, and the standard deviation of DDIFPA is 27 orders of magnitude higher than ABC.Figures6 and 7 show the graphical analysis results of the ANOVA tests. Figure 6 shows that RCBBO, ABC, and DDIFPA can obtain the relatively stable optimal values. Figure 7 shows that when solving function f10, most of the algorithms can obtain the stable optimal value after 20 independent runs.Figure 6
ANOVA tests forf07.Figure 7
ANOVA tests forf10.Figures8 and 9 show the fitness function curve evolution. From Figure 9, we can conclude that both ABC and DDIFPA converge to the optimal solution. From Figure 9, we can conclude that DDIFPA converges to a more precise point than other algorithms, and its convergence speed is faster.Figure 8
Fitness function curve evolution forf07.Figure 9
Fitness function curve evolution forf10.From Table2, f11 and f12 are multimodal low-dimensional functions. For f11, the solutions of most of the algorithms are accurate in 3 to 4 decimal places, and the rank of DDIFPA is second. For f12, the rank is second too, and the experiment results show that DDIFPA can do a good job in solving multimodal low-dimensional problems.


## 3.3. Experimental Analysis

We have carried out benchmark validations for unimodal and multimodal test functions using the proposed algorithm (DDIFPA) with three improvement strategies (local neighborhood search strategy, dimension by dimension evaluation and improvement strategy, and dynamic switching probability strategy). An optimization process can be divided into two key components (local search and global search); we use a dynamic switching probabilityp∈[0,1] to control the whole searching process. LNSS and DDEIS are applied to the local search process and enhance its exploitation ability. Among 12 test functions listed above, f01tof05 are unimodal, and the remarkable achievements confirm that DDIFPA have stronger exploitation ability than FPA and other algorithms. And DSPS, which could improve the ability of escape from poor local optima, was applied to enhance the exploration ability. That also balanced exploitation and exploration dynamically. For multimodal benchmark functions (f06tof12), we can conclude that DDIFPA converges to a more precise point than other algorithms, and its convergence speed is faster. Our simulation results for finding the global optima of various test functions suggest that DDIFPA can outperform the FPA and other mentioned algorithms in terms of both precision and convergence speed.


## 3.4. High-Dimensional Functions Test

In previous sections, 12 standard test functions are applied to evaluate the optimal performances of the FPA with dimension by dimension improvement (DDIFPA) in the case of low dimension. In order to evaluate the performances of DDIFPA comprehensively, we also do some high-dimensional tests inf1,f2,f4,f7,f10. The test results are shown in Table 3. As can be seen in Table 3, DDIFPA can also solve high-dimensional problems efficiently and stably.


## 4. Conclusions

In this paper, three optimization strategies (local neighborhood search strategy, dimension by dimension evaluation and improvement strategy, and dynamic switching probability strategy) have been applied to FPA to improve its deficiencies. By 12 typical standard benchmark functions simulation, the results show that DDIFPA algorithm generally has strong global searching ability and local optimization ability, and effectively avoid the defects of other algorithms fall into local optimization. DDIFPA has improved the convergence speed and convergence precision of FPA. The experiment results show that it is an effective algorithm to solve complex functions optimization problems.In this paper, we only consider the global optimization. The algorithm can be extended to solve other problems such as constrained optimization problems and multiobjective optimization problem. In addition, many engineering design problems are typically difficult to solve. The application of the proposed FPA with dimension by dimension improvement in engineering design optimization may prove fruitful.



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*Source: 481791-2014-09-01.xml*