Particle swarm optimization algorithm with proportional factor based on Nash equilibrium
Wenxin Luo · Journal of Physics Conference Series · 2022
Abstract Based on the global gradient-less stochastic search method of traditional particle swarm optimization algorithm, an improved particle swarm optimization algorithm for finding mixed-strategy Nash equilibrium is proposed in view of the shortcomings of prone particle overflow and unconstrained relationship between individual dimensions. In the improved evolution equation, the position function is improved to conform to the concept and properties of the combination of strategies, and the proportional factor is introduced to ensure that the numerical value of particle iteration is mapped to the solution space of normal form game. Through the test of several groups of classical examples, the results show that the algorithm not only improves efficiency and accuracy, but also solves the problems of divergence of particle and low efficiency of iteration. The experiments show that the algorithm has good practical performance.