一种自适应惯性权重的粒子群优化算法

A PARTICLE SWARM OPTIMISATION WITH ADAPTIVE 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(Particle Swarm Optimization)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 capability of the particle swarm optimisation,the relationship between the inertia weight,the particle fitness and population size,as well as the dimensions of searching space is analysed,and the particle inertia weight is defined as a function of them three.By updating the inertia weight of every particle in each iteration,the self-adaptive adjustment between global search ability and local search ability is achieved.An improved particle swarm optimisation is brought up in combination with the population dynamic management strategy.The new algorithm is proved to have stronger global optimisation capability and higher search efficiency through the comparison of it with existing inertia weight adjustment algorithms using a couple of commonly used testing functions.