Particle Swarm Optimization with a Simulated Binary Crossover Operator

Lei Yang, Caixia Yang, Yu Liu


Particle swarm optimization (PSO) is a new intelligent search technique, which is inspired by swarm intelligence. Although PSO has shown good performance in many benchmark optimization problems, it suffers from premature convergence in solving complex multimodal problems. In this paper, we propose a novel PSO algorithm, called PSO with a simulated binary crossover operator (SCPSO), to improve the performance of PSO. Experimental results on several benchmark problems show that SCPSO achieves better performance than standard PSO. 

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