一种局部搜索能力增强的狮群算法

An enhanced local search lion optimization algorithm

狮群算法作为一种新型群智能优化算法,其进化过程多依据狮群猎食、交配等动物本性出发,因此难免会存在收敛速度慢并且不容易发现全局极值等缺点.针对当前基本狮群算法存在的缺点,提出一种局部搜索能力增强的狮群算法(Enhanced Local Search Lion Optimization Algorithm,ELSLOA).为增强种群局部搜索效率,对所有领地狮引入对立搜索方法提高寻优能力,并对优良个体执行Levy flight操作,提高个体局部开采能力,最后利用Tent混沌搜索对领地狮和流浪狮执行混沌操作.对算法进行了函数的仿真对比分析,充分验证了所提出算法的优良性能.

Lion algorithm is a new swarm intelligent evoluationary optimization algorithm,and the evoluation process almostly follow the animal nature,such as prey and mating,so inevitably easy trap into local optimutun.Aiming at the deficiency of the basic lion algorithm,this paper proposes a new modified version,named as enhanced local search lion optimization algorithm(ELSLOA).In order to enhance the local evolution efficiency,the territorial lion individual can be updated with opposition-based learning method,and the excellent individual perform the Levy flight operation in order to enhance the local exploitation ability.Tent chaos search operation can be executed on the population.Simulation of benchmark functions proved that the proposed algorithm performs well than other algorithms.