A Modified Two Sub-swarms Exchange Particle Swarm Optimization

Particle swarm optimization and its modification for two sub-swarms exchange appear premature convergence for complex optimization problem, because particles’ performance becomes same in seeking later period. Therefore, in this paper, a modified two sub-swarms exchange particle swarm optimization is proposed. The particle swarm is divided into two identical sub-swarms, with the first adopting the standard PSO model, and the second adopting the Cognition Only model. When the two sub-swarms evolve steady states independent, a certain amount of particles of the second sub-swarm that are extracted randomly exchange with the worst fitness value of particles of the first sub-swarm, which can increase the information exchange between the particles, improve the diversity of population and meliorate the convergence of algorithm. Four complex testing functions’ results indicate that the proposed algorithm has greater globally optimal solution,better optimal efficiency and better performance than PSO and TSE-PSO in many aspects.