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.

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