一种多策略混合的粒子群优化算法

针对传统粒子群优化算法在解决一些复杂优化问题时易陷入局部最优且收敛速度较慢的问题,提出一种多策略混合的粒子群优化算法(Hybrid Particle Swarm Optimization with Multiply Strategies,HPSO)。该算法利用反向学习策略产生反向解群,扩大粒子群搜索的范围,增强算法的全局勘探能力；同时,为避免种群陷入局部最优,算法对种群中部分较差的个体实施柯西变异,以产生远离局部极值的个体,而对群体中较好的个体施以差分进化变异,以增强算法的局部开采能力。对这3种策略进行了有机结合以更好地平衡粒子群算法全局勘探和局部开采的能力。将HPSO算法与其他3种知名的粒子群算法在10个标准测试函数上进行了性能比较实验,结果表明HPSO算法在求解精度和收敛速度上具有较显著的优势。

Hybrid Particle Swarm Optimization with Multiply Strategies

A hybrid particle swarm optimization with multiply strategies (HPSO) was proposed to solve the problem of being easy to get into the local optimum and slow convergence speed for particle swarm optimization algorithm(PSO) in dealing with some complicated optimization problems.The HPSO uses the opposition-based learning strategy to genera-te the opposition-based solutions,which enlarges the search range of particle swarm,and enhances the global exploration ability of the algorithm.At the same time,in order to jump out of the local optimum,the HPSO performs Cauchy mutation on some poorer particles to generate individuals that are far from the local optimum,and the differential evolution (DE) mutation is employed to remain individuals to improve the capacity of local exploitation.The above strategies are combined to balance the abilities of global exploration and local exploitation,which are expected to solve some hard optimization problems better.The HPSO and other three well-known PSOs were compared on 10 benchmark test instances experimentally.The results show that the HPSO performs significant advantages over the compared algorithms in the solution accuracy and the convergence speed.