一种新型粒子群优化算法

A new type of particle swarm optimization

为提高基本粒子群算法的搜索效率,引入和声算法中产生新解的策略(称之为和声策略),综合粒子自身经验和社会认知两方面的信息直接更新粒子的位置,提出了基于和声策略的新型粒子群优化算法,通过对高维复杂函数的优化分析比较结果表明,基于新型粒子群优化算法的搜索能力较基本粒子群优化算法大大提高。本算法对其它智能算法具有借鉴意义。

In order to enhance the efficiency of original particle swarm optimization algorithm,the harmony search procedure is adopted to generate the new positions of current particles using self-learning and social recognition.Therefore,a new type of particle swarm optimization algorithm has been developed,which is used to solve numerical functions.A comparative study shows that the new particle swarm optimization algorithm enhances the efficiency in searching capacity than that of the original PSO.