精英协同进化的蜉蝣算法

Elite coevolutionary mayfly algorithm

蜉蝣算法的种群多样性低、寻优性能差,为此提出基于精英协同进化的蜉蝣算法(ECMA).将雄性蜉蝣种群根据自身种群适应度分为精英种群和普通种群,精英个体进行自我学习以保持种群的多样性,实现高水平的全局搜索;普通个体飞向统一目标进行局部开发,以提高ECMA的收敛速度.根据婚姻市场理论改进雌性蜉蝣的位置更新,提高ECMA的寻优性能;引入新的自适应重力系数平衡全局搜索和局部开发能力,提高ECMA的收敛精度;引入莱维飞行的跳出策略,避免ECMA陷入局部最优.基于20个基准测试函数和CEC2019测试函数进行算法的仿真优化分析,与蜉蝣算法以及其他优秀的群智能算法相比,ECMA在寻优精度、收敛速度和稳定性方面均有较大提升.

An elite coevolutionary mayfly algorithm(ECMA)was proposed to resolve the small population diversity and the poor optimization performance of the mayfly algorithm.Firstly,male mayflies were divided into elite and ordinary members based on their fitness performance,then the elite individuals learned from itself to maintain the population diversity and achieve high-level global search,while the ordinary individuals flew toward a unified target for local development to improve the convergence speed of ECMA.Secondly,the position update of female mayflies was improved based on the marriage market theory,thus enhancing the optimization performance of ECMA.Thirdly,a new adaptive gravity coefficient was introduced to establish a balance between the global search and the local development to improve the convergence accuracy of ECMA.Finally,a jump-out strategy of Levy flight was introduced to avoid ECMA falling into a local optimum.Based on 20 benchmark test functions and CEC2019 test functions,the simulation optimization analysis of the algorithm was carried out.Compared with the mayfly algorithm and other excellent swarm intelligence algorithms,ECMA has greatly improved the optimization accuracy,convergence speed and stability.