粒子群优化算法的改进及数值仿真

Improvement of Particle Swarm Optimization Algorithm and Numerical Simulation

提出一种改进的粒子群优化算法,该算法采用使全局探索与局部开发合理平衡的方法,降低了粒子群优化易陷入早熟收敛的可能性.先用Beta分布初始化种群,再用逆不完全Γ函数更新惯性权重,然后基于差分进化的新算子实现速率更新,最后采用基于边界对称映射的方法处理粒子的越界.数值仿真结果表明,改进算法明显优于普通粒子群优化算法、差分进化算法、人工蜂群优化算法和蚁群优化算法.

We proposed an improved particle swarm optimization（PSO）algorithm.The algorithm used reasonable balance between the global exploration and local development,which reduced the possibility of premature convergence of PSO.Firstly,the Beta distribution was used to initialize population.Secondly,the inverse incompleteΓ function was used to update the inertia weight.Thirdly,a new operator based on differential evolution was introduced to adjust the velocity.Finally,we used the method based on boundary symmetry mapping to deal with the cross boundary of particles.Numerical simulation results show that the improved algorithm is obviously superior to the common PSO algorithm,differential evolution algorithm,artificial bee colony optimization algorithm and ant colony optimization algorithm.