一种新的基于提高多样性的粒子群优化算法

Particle Swarm Optimization based on improving diversity

提出了一种基于提高多样性的粒子群优化算法。在速度更新公式中,将比当前粒子适应度更高的其它所有粒子的个体最优位置信息进行加权学习;在位置更新公式中,利用真实物理反弹理论将解空间外的粒子反弹回解空间内。5个基准测试函数的仿真实验表明,该算法能有效克服PSO中的过早收敛问题,并显著提高粒子的多样性,同时有效控制粒子的进化速度。

In this paper a novel Particle Swarm Optimization algorithm（Particle Swarm Optimization based on Improving Diversity, PSO-ID） is developed.The new method introduces the information of all the particles which has better fitness values than the current particle into the speed reform function.Considering that whichever particle moves out of the boundary in each dimension of solution space,the physical reflection theory is adopted to improve the efficient variety of the particle swami.The method is applied to 5 test functions,and the simulation result shows that the PSO-ID is more efficient and precise than the standard PSO. It can also escape from the local minimum.