一种自适应惯性权重的粒子群优化算法

Adaptive Particle Swarm Optimization Algorithm with Dynamically Changing Inertia Weight

为较好平衡粒子群算法中全局搜索能力与局部搜索能力,分析了PSO算法中的惯性权重与种群规模、粒子适应度以及搜索空间维度的关系,并把粒子惯性权重定义为这三者的函数.通过在每次迭代后更新每个粒子的惯性权重,实现了自适应调整全局搜索能力与局部搜索能力,并结合动态管理种群的策略提出了改进的粒子群算法.通过在多个常用测试函数上与已有惯性权重调整算法测试比较,证明新算法具有较强的全局寻优能力与较高的搜索效率.

In order to get a better balance between global search ability and local search capabilities in the particle swarm algorithm,analyzed the relationship between inertia weight and the particle fitness,the population size and dimensions of the searching space,and constructed a function between them.After each iteration,updated the inertia weight of each particle as to achieved a self-adaptive adjustment of global search ability and local search capabilities.A new improved particle swarm optimization is brought up combined with population dynamic management strategy.The searching result of some standard testing functions proved that the new algorithm have a stronger global optimization capability and a higher search efficiency.