An improved predator-prey particle swarm optimization algorithm for Nash equilibrium solution.

Focusing on the problem incurred during particle swarm optimization (PSO) that tends to fall into local optimization when solving Nash equilibrium solutions of games, as well as the problem of slow convergence when solving higher order game pay off matrices, this paper proposes an improved Predator-...

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Autores principales:	Yufeng Meng, Jianhua He, Shichu Luo, Siqi Tao, Jiancheng Xu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7ae3e1482e964be88d7b616d6e3fc2b9
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spelling	oai:doaj.org-article:7ae3e1482e964be88d7b616d6e3fc2b92021-12-02T20:16:08ZAn improved predator-prey particle swarm optimization algorithm for Nash equilibrium solution.1932-620310.1371/journal.pone.0260231https://doaj.org/article/7ae3e1482e964be88d7b616d6e3fc2b92021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0260231https://doaj.org/toc/1932-6203Focusing on the problem incurred during particle swarm optimization (PSO) that tends to fall into local optimization when solving Nash equilibrium solutions of games, as well as the problem of slow convergence when solving higher order game pay off matrices, this paper proposes an improved Predator-Prey particle swarm optimization (IPP-PSO) algorithm based on a Predator-Prey particle swarm optimization (PP-PSO) algorithm. First, the convergence of the algorithm is advanced by improving the distribution of the initial predator and prey. By improving the inertia weight of both predator and prey, the problem of "precocity" of the algorithm is improved. By improving the formula used to represent particle velocity, the problems of local optimizations and slowed convergence rates are solved. By increasing pathfinder weight, the diversity of the population is increased, and the global search ability of the algorithm is improved. Then, by solving the Nash equilibrium solution of both a zero-sum game and a non-zero-sum game, the convergence speed and global optimal performance of the original PSO, the PP-PSO and the IPP-PSO are compared. Simulation results demonstrated that the improved Predator-Prey algorithm is convergent and effective. The convergence speed of the IPP-PSO is significantly higher than that of the other two algorithms. In the simulation, the PSO does not converge to the global optimal solution, and PP-PSO approximately converges to the global optimal solution after about 40 iterations, while IPP-PSO approximately converges to the global optimal solution after about 20 iterations. Furthermore, the IPP-PSO is superior to the other two algorithms in terms of global optimization and accuracy.Yufeng MengJianhua HeShichu LuoSiqi TaoJiancheng XuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0260231 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yufeng Meng Jianhua He Shichu Luo Siqi Tao Jiancheng Xu An improved predator-prey particle swarm optimization algorithm for Nash equilibrium solution.
description	Focusing on the problem incurred during particle swarm optimization (PSO) that tends to fall into local optimization when solving Nash equilibrium solutions of games, as well as the problem of slow convergence when solving higher order game pay off matrices, this paper proposes an improved Predator-Prey particle swarm optimization (IPP-PSO) algorithm based on a Predator-Prey particle swarm optimization (PP-PSO) algorithm. First, the convergence of the algorithm is advanced by improving the distribution of the initial predator and prey. By improving the inertia weight of both predator and prey, the problem of "precocity" of the algorithm is improved. By improving the formula used to represent particle velocity, the problems of local optimizations and slowed convergence rates are solved. By increasing pathfinder weight, the diversity of the population is increased, and the global search ability of the algorithm is improved. Then, by solving the Nash equilibrium solution of both a zero-sum game and a non-zero-sum game, the convergence speed and global optimal performance of the original PSO, the PP-PSO and the IPP-PSO are compared. Simulation results demonstrated that the improved Predator-Prey algorithm is convergent and effective. The convergence speed of the IPP-PSO is significantly higher than that of the other two algorithms. In the simulation, the PSO does not converge to the global optimal solution, and PP-PSO approximately converges to the global optimal solution after about 40 iterations, while IPP-PSO approximately converges to the global optimal solution after about 20 iterations. Furthermore, the IPP-PSO is superior to the other two algorithms in terms of global optimization and accuracy.
format	article
author	Yufeng Meng Jianhua He Shichu Luo Siqi Tao Jiancheng Xu
author_facet	Yufeng Meng Jianhua He Shichu Luo Siqi Tao Jiancheng Xu
author_sort	Yufeng Meng
title	An improved predator-prey particle swarm optimization algorithm for Nash equilibrium solution.
title_short	An improved predator-prey particle swarm optimization algorithm for Nash equilibrium solution.
title_full	An improved predator-prey particle swarm optimization algorithm for Nash equilibrium solution.
title_fullStr	An improved predator-prey particle swarm optimization algorithm for Nash equilibrium solution.
title_full_unstemmed	An improved predator-prey particle swarm optimization algorithm for Nash equilibrium solution.
title_sort	improved predator-prey particle swarm optimization algorithm for nash equilibrium solution.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7ae3e1482e964be88d7b616d6e3fc2b9
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An improved predator-prey particle swarm optimization algorithm for Nash equilibrium solution.

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