Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation  

作　　者：Yan Wu Genqin Sun Keming Su Liang Liu Huaijin Zhang Bingsheng Chen Mengshan Li 

机构地区：[1]Gannan Normal University, Ganzhou, China

出　　处：《Journal of Computer and Communications》2017年第13期9-20,共12页电脑和通信（英文）

摘　　要：In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems.In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems.

关 键 词：Improved Particle SWARM Optimization Algorithm Double POPULATIONS MULTI-OBJECTIVE Adaptive Strategy CHAOTIC SEQUENCE 

分 类 号：P1[天文地球—天文学]