平成 26 年度電気関係学会四国支部連合大会 講演論文集 （2014 徳島大学）
2014 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (TOKUSHIMA)

1-26Competitive Particle Swarm Optimization with Secret Agent(C-PSO-SA)
Yasutaka SHIMIZU1 Norio INUKAI 1 Yoko UWATE1(1 Tokushima University)

Yoshifumi NISHIO1

3. Numerical Experiments

1. Introduction
Particle Swarm Optimization (PSO) is known as oneof Swarm Intelligence. PSO algorithm is modeled aftergeneric swarm. PSO is easily possible to find solutions.
In this study, we propose Competitive PSO with SecretAgent (C-PSO-SA). C-PSO-SA is added in excessive advertising to be based on PSO. We investigate behavior ofC-PSO-SA by computer simulation.

2. C-PSO-SA algorithm
PSO algorithm is used two expression from Eqs. (1) and(2).

In order to confirm the performance of PSO and C-PSOSA algorithm, we apply PSO and C-PSO-SA to the twofunction (Table 1).

Table 1: Optimization Test Functions
nameFormulaRastrigin

f (x) =(2) ⃗vk : particle velocity⃗
xk : particle positionp⃗1 : own previous best positionp⃗2 : globally best position in the whole swarm ⃗a, ⃗b1 , ⃗b2 , ⃗c, d⃗ : coeﬃcient⃗r1 , ⃗r2 : random numbers.

The algorithm of the standard PSO and C-PSO-SA isdescribed below;
Step 1 { Decide for ⃗v0 , ⃗x0 , p⃗1 , p⃗2 }⃗v0 and ⃗x0 are decided random number for all particle.
p⃗1 and p⃗2 are decided from Eq. (3).
p
⃗1 = min{⃗xk }, p⃗2 = min{⃗p1 }Griewank1f (x) = 4000

In this study, we decide coeﬃcients; a = 0.6, b1 = b2 =1.7 and c = d = 1.

26n∑2xi −i=1n∏xicos( √ ) + 1ii=1

In the simulation, we apply 30 dimension to test function. The number of particle is 30, the simulation times
is 100. The results are shown in Table 2. Evaluated value
is defined by the Eq. (4).
(N umber of particle) × (Average)(Evaluated value) =(Success rate)(4)
We can not observe a change of results in Rastrigin function. However, the result of C-PSO-SA is better than PSOin Griewank function. Because, we consider to be due tomutate dimension on this study in Step 4.

Table 2: Result of Optimization(3)

Step 2 { Update for ⃗vk , ⃗xk }Eqs. (1) and (2) decide to update ⃗vk , ⃗xk .
Step 3 { Update for p⃗1 , p⃗2 }Eq. (3) decides on updating p⃗1 , p⃗2 .
Step 4 { Secret Agent section }We change the value of p
⃗2 . In addition, we change p⃗2 inincrement of one dimensions. Finally, two swarm provide
each other p⃗2 .
Step 5 { End decision }If p⃗2 is smaller than the threshold value, the algorithm
ends. Also, if k (loop count) measure up to the thresholdvalue, the algorithm ends.

(x2i − 10 cos(2πxi ) + 10)

i=1⃗vk+1 = ⃗a ⊗⃗vk +⃗b1 ⊗⃗r1 ⊗ (⃗p1 −⃗xk ) +⃗b2 ⊗⃗r2 ⊗ (⃗p2 −⃗xk ) (1)⃗xk+1 = ⃗c ⊗ ⃗xk + d⃗ ⊗ ⃗vk+1n∑

Rastrigin

Griewank

Average
Success rate
Evaluated value
Average
Success rate
Evaluated value

PSO
429.4691
141.58617.3489
208.09C-PSO-SA
429.4691
141.58685.56
100205.66

4. Conclusions
In this study, we proposed Competitive Particle Swarm
Optimization with Secret Agent(C-PSO-SA). We investigated behavior of C-PSO-SA by the simulation and confirmed the eﬃciency. In the future work, we would like toapply a lot of functions.
Reference[1] Trelea, I. C. “The particle swarm optimization algorithm: convergence analysis and parameter selection”, Information processing letters, 85(6), pp. 317–325, 2003.