Differential evolution particle swarm optimization algorithm based on good point set for computing Nash equilibrium of finite noncooperative game

In this paper, a hybrid differential evolution particle swarm optimization (PSO) method based on a good point set (GPDEPSO) is proposed to compute a finite noncooperative game among N people. Stochastic functional analysis is used to prove the convergence of this algorithm. First, an ergodic initial...

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Vydané v:AIMS mathematics Ročník 6; číslo 2; s. 1309 - 1323
Hlavní autori: Li, Huimin, Xiang, Shuwen, Yang, Yanlong, Liu, Chenwei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: AIMS Press 01.01.2021
Predmet:
differential evolution particle swarm optimization algorithm
good point set
high-dimensional payoff matrix
nash equilibrium
non-cooperative game
ISSN:2473-6988, 2473-6988
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Abstract In this paper, a hybrid differential evolution particle swarm optimization (PSO) method based on a good point set (GPDEPSO) is proposed to compute a finite noncooperative game among N people. Stochastic functional analysis is used to prove the convergence of this algorithm. First, an ergodic initial population is generated by using a good point set. Second, PSO is proposed and utilized as the variation operator to perform variation crossover selection with differential evolution (DE). Finally, the experimental results show that the proposed algorithm has a better convergence speed, accuracy, and global optimization ability than other existing algorithms in computing the Nash equilibrium of noncooperative games among N people. In particular, the efficiency of the algorithm is higher for determining the Nash equilibrium of a high-dimensional payoff matrix game.
AbstractList In this paper, a hybrid differential evolution particle swarm optimization (PSO) method based on a good point set (GPDEPSO) is proposed to compute a finite noncooperative game among N people. Stochastic functional analysis is used to prove the convergence of this algorithm. First, an ergodic initial population is generated by using a good point set. Second, PSO is proposed and utilized as the variation operator to perform variation crossover selection with differential evolution (DE). Finally, the experimental results show that the proposed algorithm has a better convergence speed, accuracy, and global optimization ability than other existing algorithms in computing the Nash equilibrium of noncooperative games among N people. In particular, the efficiency of the algorithm is higher for determining the Nash equilibrium of a high-dimensional payoff matrix game.
Author Liu, Chenwei
Yang, Yanlong
Li, Huimin
Xiang, Shuwen
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CorporateAuthor College of mathematics and statistics, Guizhou University, Guiyang 550025, China
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SubjectTerms differential evolution particle swarm optimization algorithm
good point set
high-dimensional payoff matrix
nash equilibrium
non-cooperative game
Title Differential evolution particle swarm optimization algorithm based on good point set for computing Nash equilibrium of finite noncooperative game
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