Abstract Particle swarm optimization(PSO) is easily trapped in local optimum and stagnation, and therefore a self-adaptive ejector particle swarm optimization algorithm(SAEPSO) is proposed. To keep the vitality of the particle swarm, the ejector operation is introduced into the algorithm. While the satisfying the condition, the particle is given a high speed at the current position to fly to a faraway area. Full-dimensional ejection and probabilistic ejection can be selected for the ejection mode. To cope with the ejector operation, a new quality judgment for particles is proposed, so particles can be ejected out of the feasible region. A self-adaptive discrimination function is introduced in the proposed algorithm to judge whether the particle should be ejected. While satisfying the function, the particles are ejected. Numerical experiments show that the proposed algorithm possesses relatively strong global search ability and fast search speed.
Fund:Supported by National Natural Science Foundation of China(No.61671470), Natural Science Foundation of Jiangsu Province(No.BK20161470)
About author:: (ZHU Jingwei(Corresponding author), Ph.D, lecturer. His research interests include intelligent computing and robotics.)(FANG Husheng, master, associate professor. His research interests include electro-mechanical control technology and robotics.)(SHAO Faming, master, lecturer. His research interests include electromechanical control technology and intelligent computing.) (JIANG Chengming, master, associate professor. His research interests include electromechanical control technology and intelligent computing.)
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2019, Vol. 32 
