平成25年度電気関係学会四国支部連合大会 講演論文集 （2013 徳島大学）
2013 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (TOKUSHIMA)

1-27
Particle Swarm Optimization with Predator
Norio INUKAI†

Takuya INOUE† Yoko UWATE†(Tokushima University)

Yoshifumi NISHIO†

1. Introduction

3. Numerical Experiments

Particle Swarm Optimization (PSO) is known as one ofSwarm Intelligence. PSO algorithm is used for the systemof fish school. It is simple and easily possible to find solutions. However, PSO algorithm is diﬃcult to escape fromlocal minimum.
In this study, we propose PSO with Predator (PSOP). PSO-P is added in relationship between small fish andpredator to be based on PSO, and the feature of PSO-P
is easy to escape local minimum. We investigate behaviorof PSO-P by computer simulation.

In order to confirm the performance of PSO and PSO-P
algorithm, we apply PSO and PSO-P to the three function(Table 1).

2. PSO-P algorithm

Table 1: inspection function
nameSphereRastrigin
Griewank

expression
f (x) = x2
f (x) = x2 − 10 cos(2πx) + 10x2
f (x) = 4000− cos(x) + 1

PSO algorithm uses two expression from Eqs. (1), (2),because we simulation one dimensional it.
vk+1 = a × vk + b1 × r1 × (p1 − xk ) + b2 × r2 × (p2 − xk ) (1)xk+1 = c × xk + d × vk+1

Particle of number is 100, the simulation times is 100.
The results are shown in Table 2. The unit of Table 2 ismilliseconds.

(2)

In this time, expression values decide by Eqs. (1), (2):vk : particle velocityxk : particle position

Table 2: Result of Optimization

p1 : own previous best position

Spherep2 : globally best position in the whole swarma, b1 , b2 , c, d : coeﬃcient
r1 , r2 : random numbers

Rastrigin

The algorithm of standard PSO and PSO-P is described
below;
Step 1 { Decide for v0 , x0 , p1 , p2 }v0 and x0 decide random number for all particle. p1
and p2 decide from Eq. (3).
p1 = min{xk }, p2 = min{p1 }(3)Step 2 { Update for vk , xk }Updating vk , xk , using for Eqs. (1), (2).
Step 3 { Update for p1 , p2 }Updating p1 , p2 , using for Eq. (3).
Step 4 { Predator section }Predation decision do for particle around predator. If
it is true, we reposition the half of particle that are preyed
particle.
Step 5 { End decision }If p2 is smaller than the threshold value, the algorithm
ends. Also, if k (loop count) measure up to the thresholdvalue, the algorithm ends.
The loop of standard PSO is Step 2, 3, 5. The loop ofPSO-P is Step 2–5.
In this study, we decide coeﬃcient;a = 0.6, b1 = b2 = 1.7, c = d = 1.

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Griewank

AVE
MIN
MAX
AVE
MIN
MAXAVE
MIN
MAX

PSO
824
550
1350
817.8
560
1410
39.1
3050

PSO-P
911.5
520
1490
901
540
2170
38.5
3050

Table 2; PSO-P is more quickly PSO in minimumof Sphere function, Rastrigin function, and average ofGriewank function. However, other results of PSO-P areslower or equal. Because this study used to one dimensional PSO.

4. Conclusions
In this study, we proposed PSO with Predator (PSOP). We investigated behavior of PSO-P by the simulationand confirmed the eﬃciency. In the future work, we would
like to apply high dimensional PSO.
Reference[1] Trelea, I. C. “The particle swarm optimization algorithm: convergence analysis and parameter selection”,
Information processing letters, 85(6), pp. 317–325, 2003.