基于混沌搜索的思维进化算法

Mind evaluation algorithm based on chaos searching

针对思维进化算法中的产生初始种群的盲目随机性和冗余性以及现有搜索方式易陷入局部最优的问题,将混沌优化和思维进化算法结合,提出了一种基于混沌搜索的思维进化算法(Chaos Mind Evaluation Algorithm,CMEA)。该算法在进化的不同阶段引入混沌优化操作,利用混沌的遍历性提高算法的收敛速度,克服了早熟现象,同时利用思维进化算法的记忆特性和当代最优解指导混沌搜索,提高算法的搜索能力。仿真结果表明,与标准思维进化相比,该算法优化能力强,能有效地避免局部收敛,具有更快的收敛速度。

Due to the disadvantages of Simple Mind Evaluation Algorithm（SMEA）,such as the generation of the initial population is blind,random and redundant,so it is easy to get into part extremum solution,a mixed optimal algorithm CMEA（Chaos Mind Evaluation Algorithm） is proposed in the paper combining the chaotic optimal algorithm and MEA.In this method,the chaos optimization is introduced in different phase of population evolution.The new algorithm makes use of the ergodicity of chaos to improve the convergence rate and overcome the local convergence.The character of memory and optimum solution of the present generation are used to instruct the chaos search to improve searching efficiency.Simulation results show that the proposed algorithm can remarkably improve optimization performance and avoids local convergence while producing a high convergence rate.