应用混沌变异机制混合反向学习人工鱼群算法

Hybrid Opposition-based Learning and Artificial Fish Swarm Algorithm Using Levy Flight

提出了一种应用反向学习和Tent混沌映射变异的改进人工鱼群算法.选择部分精英个体执行反向学习,引导种群向全局最优解逼近,有利于均衡算法的勘探与开采能力.当种群多样性下降到一定程度,并且公告板多次无法得到更新时,保留部分精英个体,其他个体执行Tent混沌映射变异操作.在4个Benchmark函数上的测试表明该算法求解精度、收敛能力较AFSA有较大提高.

The optimization mechanism and deficiency of AFSA were analyzed,and an improved AFSA using Opposition-based Learning（OBL）and Tent chaotic map was proposed.Choosing some elited individuals exeute with OBL to guid the search space to approach the space in which the global optimum is included.This mechanism is helpful to get tradeoff between exploration and exploitation ability of AFSA.When the diversity of population descend to a limen,preserving some elites others excute Tent map variation.Simulation results for 4benchmark functions show that the proposed algorithm has higher precision and global optimization ability than AFSA.