Abstract: Aimed at solving the defects of premature and easy being trapped into the local optimum of particle swarm optimization (PSO),a new algorithm is proposed with considering the species group structure,multi-mode learning and individual game,which was named as multi-subgroup particle swarm optimization algorithm with game theory (MPSOGT).The proposed algorithm constructs a dynamic multi-subgroup structure and introduces different learning strategies to form a multi-source learning strategy with heterogeneous multiple subgroups.Then the evolutionary game theory is introduced into the process of population sear-ching.According to a dynamic payoff matrix and a dynamic rooted probability based on the game theory,each individual enters into a suitable subgroup randomly to enhance its searching ability.Based on twelve benchmark functions,combined experiments are carried out for subgroup size L.The results show that the population fitness and median have obvious advantages when the value of L is N/2 or N/3.Compared with seven similar algorithms under a set of twelve benchmark functions test with different scales,the results show that the performance of the improved algorithm is superior to the comparison algorithm in terms of optimal va-lue,solution stability and convergence characteristics.It is indicated that the proposed multi-source learning and game probability selecting strategies can effectively improve the performance of the PSO algorithm.

Key words: Dynamic heterogeneous subgroups, Game selection, Particle swarm optimization, Payoff matrix, Root probability