A two Sub-swarm Exchange Particle Swarm Optimization considering exploration and exploitation

Particle swarm optimization and its modifications appear premature convergence for complex optimization problem, because particles´ performance becomes same in seeking later period. In this paper, A new model is proposed to avoiding particles´ performance same and possessing strong exploration capacity. Considering exploration and exploitation capacity diverse in different stage, the particle swarm is divided into two identical sub-swarms, with the first adopting the standard PSO model, and the second adopting the proposed model. When the two sub-swarms evolve steady states independent, a certain amount of particles of the first sub-swarm that are extracted randomly exchange with the worst fitness value of particles of the second 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.