Research Of Hybrid Particle Swarm Optimization And Its Application

With the development of economic society, the optimization problem becomes more and more complex, and the traditional optimization algorithm cannot solve the problem. Owing to the unique advantages of swarm intelligence optimization algorithms in solving complex optimization problems, it has been paid attention by the majority of researchers. Particle Swarm Optimization is an important algorithm of Swarm intelligence optimization algorithms, which has the characteristics of simple implementation, less parameters, better optimization effect and so on. It has been widely studied in the field of science and engineering.Particle Swarm Optimization also has some defects, such as easy to fall into local optimum and convergences slowly, which seriously restrict its application. In order to solve these problems, the paper presents a new algorithm which is named particle health degree based artificial bee colony particle swarm optimization(HABCPSO). By dynamically evaluating the health status of each particle, the normal and pathological particles are processed separately to avoid invalid search and to improve the convergence speed of the algorithm. To avoid fall into local optimum, on one hand, when dealing with the pathological particles, using artificial colony algorithm of search strategy to improve exploration ability by large probability; on the other hand, increasing the diversity of particle swarm by small probability. Through the experiments on twelve standard test functions show that the proposed algorithm can avoid falling into local optimization and significantly improve convergence speed.To the problems of slow convergence and easy to fall into local optimization appeared in standard particle swarm optimization, the regulations of particles’ compaction and scheduling are presented in this paper, a Particle Compaction and Scheduling based Particle Swarm Optimization(PCS-PSO) algorithm is proposed. In order to avoid staying in local optimization, PCS-PSO evaluates dynamically particle’s compaction and schedules the particle when the value of the particle’s compaction is beyond the threshold. Through a lot of simulation experiments and comparison with other algorithms, it is proved that the PCS-PSO can avoid falling into local optimization and improve convergence speed significantly.Logistics location allocation problem is the NP hard problem with complex constrained nonlinear programming. In this paper, the particle swarm optimization algorithm is applied to the logistics location problem, through a lot of simulation experiments and compared with other algorithms, the experimental results show that the HABCPSO and PCS-PSO algorithms have higher searching ability, faster searching speed and higher application value.