Fault Indicator Configuration Optimization Based on Cooperative Game Particle Swarm Algorithm

doi:10.16182/j.issn1004731x.joss.22-0192

Abstract

Abstract:

In order to balance the reliability and economy of distribution network fault indicator, a multi-space cooperative game particle swarm optimization algorithm is proposed. Based on the idea of population space grouping, the population activity space is adaptively divided, the particle game evolution in subspace is achieved, and the game calculation of particle fusion cosine similar reverse strategy is carried out, which well balances the convergence and diversity of the algorithm. The simulation results show that the adaptive multi-space division of population is conducive to jumping out of the local extreme value, and the game calculation integrating cosine similar reverse is conducive to improving the precision of population convergence. The method has good universality and effectiveness.

Key words: PSO, fault indicator, optimal configuration, game calculation, co-calculation

CLC Number:

TP301.6

Xu Wang, Weidong Ji, Guohui Zhou. Fault Indicator Configuration Optimization Based on Cooperative Game Particle Swarm Algorithm[J]. Journal of System Simulation, 2023, 35(6): 1278-1289.

Figures/Tables 14

Fig. 1

Fig. 2

Fig. 3

Table 1

Standard test function

函数编号	测试函数	解空间	模态
F1	Sphere Function	[-5.12, 5.12] ⁿ	单
F2	Trid Function	[-900, 900] ⁿ	单
F3	Sum of Different Powers Function	[-1, 1] ⁿ	单
F4	Dixon-Price Function	[-10, 10] ⁿ	单
F5	Sum Squares Function	[-5.12, 5.12] ⁿ	单
F6	Zakharov Function	[-10, 10] ⁿ	单
F7	Michalewicz Function	[0, $π$ ] ⁿ	多
F8	Ackley Function	[-32.76, 32.76] ⁿ	多
F9	Rastrigin Function	[-5.12, 5.12] ⁿ	多
F10	Perm Function 0, D, BETA	[-30, 30] ⁿ	多
F11	Levy Function	[-10, 10] ⁿ	多
F12	Griewank Function	[-600, 600] ⁿ	多

Table 1

Table 2

Other algorithm parameters

算法名称	算法参数
MCGPSO	$w$ (起始)=0.9， $w$ (终止)=0.4 $c 1$ = $c 2$ =1.49
PSO	$w$ (起始)=0.9， $w$ (终止)=0.4， $c 1$ = $c 2$ =1.49
GA	$P c$ =0.7， $P m$ =1/n
DE	$P c$ =0.8

Table 2

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Table 3

Table 4

Fig. 8

Fig. 9

Table 5

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测试函数	MCGPSO	PSO	GA	DE
Rank	1.50	2.41	2.75	3.33
F1	0	7.207×10^-2	6.329×10^-1	8.244×10¹
F2	-1.487×10²	-1.488×10²	-1.324×10²	-3.055×10³
F3	0	7.949×10⁴²	2.191×10⁷⁴	1.170×10¹³
F4	9.844×10^-1	7.905×10^-¹	9.675×10^-1	5.820×10⁵
F5	0	1.299×10²	1.977×10²	6.969×10²
F6	1.337	1.571	2.608	8.706×10⁴
F7	-7.359	-9.515	-9.902	-9.143
F8	8.882×10^-16	3.113	3.871	-9.143
F9	0	8.442×10¹	2.001×10²	3.403×10²
F10	4.605×10⁸⁷	3.333×10¹⁰⁶	9.194×10¹⁵⁹	3.127×10⁹⁶
F11	2.649	3.698	4.282	3.404×10⁴
F12	3.039×10^-1	5.414×10^-²	9.717×10^-1	9.869×10^-1

测试函数	MCGPSO	GOPSO	OPSO	FIPS
Rank	1.428	2.250	2.357	3.107
F2	-1.30E×10³	3.19×10⁷	3.26×10⁷	5.27×10⁷
F3	-1.20×10³	6.20×10⁹	4.03×10⁹	3.00×10⁸
F4	-1.10×10³	3.38×10⁴	3.36×10⁴	5.88×10⁴
F5	-9.95×10²	-1.00×10³	-1.00×10³	-1.00×10³
F7	-8.00×10²	-6.84×10²	-6.87×10²	-7.33×10²
F9	-6.00×10²	-5.66×10²	-5.65×10²	-5.64×10²
F10	-5.00×10²	-4.63×10²	-4.85×10²	-4.88×10²
F11	-3.68×10²	-3.46×10²	-3.43×10²	-2.97×10²
F12	-3.00×10²	-1.56×10²	-1.47×10²	-1.02×10²
F13	-2.00×10²	3.31×10¹	2.80×10¹	-1.10×10¹
F14	2.49×10³	1.42×10³	1.53×10³	5.98×10³
F15	1.00×10²	4.79×10³	5.64×10³	7.63×10³
F17	5.27×10²	3.69×10²	3.76×10²	5.02×10²
F18	4.02×10²	6.87×10²	6.57×10²	6.23×10²
F19	5.12×10²	5.06×10²	5.06×10²	5.16×10²
F20	6.00×10²	6.15×10²	6.15×10²	6.15×10²
F21	7.00×10²	1.02×10³	1.12×10³	1.03×10³
F22	3.48×10³	2.85×10³	2.66×10³	7.01×10³
F23	1.00×10³	6.15×10³	5.77×10³	8.73×10³
F27	1.50×10³	2.52×10³	2.52×10³	2.60×10³
F28	1.60×10³	2.49×10³	2.28×10³	4.41×10³

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