A Particle Swarm Optimization Algorithm Based on Differential Evolution
Gao Yue-lin · Jisuanji fangzhen · 2009
Particle swarm optimization is a simple stochastic global optimization algorithm,but it is easy to fall into partial extreme point,its convergence rate is low in the later evolution period,and the precision is low.For improving the particle swarm optimization algorithm,in this paper,differential evolution is involved into PSO algorithm.Handling the current optimum positions with mutation and crossover,and a particle swarm optimization algorithm based on differential evolution(DPSO) is proposed.In this algorithm,the detecting and exploitation abilities of both particle swarm optimization algorithm and differential evolution are utilized effectively,and the rate of progress and the efficiency are improved.The test with well-known benchmark functions shows that DPSO algorithms are better than PSO algorithm with linearly decreasing weight and differential evolution algorithm.