基于Logistic映射的蜉蝣优化算法  

Mayfly optimization algorithm based on Logistic mapping

作　　者：陈嘉豪 童楠[1] 符强[1] Chen Jiahao;Tong Nan;Fu Qiang(College of Science and Technology,Ningbo University,Ningbo,Zhejiang 315300,China)

机构地区：[1]宁波大学科学技术学院,浙江宁波315300

出　　处：《计算机时代》2021年第10期6-10,共5页Computer Era

基　　金：国家级大学生创新创业训练计划项目(202013277008);宁波市自然科学基金资助项目(202003N4159)。

摘　　要：在高维复杂问题上,蜉蝣优化算法存在易陷入局部最优区域且求解精度较差等问题,因而提出基于Logistic映射的蜉蝣优化算法。引入依据Logistic映射的混沌机制,当种群进化停滞时,当前最优蜉蝣通过混沌机制寻找适应度更好的蜉蝣,以激发种群进化能力;建立较劣蜉蝣加速进化机制,激励蜉蝣个体以达到种群寻优要求;采用动态惯性权重均衡算法全局和局部的搜索性能。抽取5个benchmark函数测试算法性能,实验结果验证了所提算法在寻优性能上的有效性。For high-dimensional complex problems,mayfly optimization algorithm has problems such as easy to fall into local optimal area and poor solution accuracy.Therefore,a mayfly optimization algorithm based on Logistic mapping is proposed.Introducing the Logistic mapping based chaotic mechanism,when the evolution of the population stagnates,the current optimal mayfly uses the chaotic mechanism to find a mayfly with better adaptability to stimulate the evolutionary ability of the population;Establishing a accelerated evolution mechanism for inferior mayfly,the mayfly individual is motivated to meet the requirements of the population optimization;Using the dynamic inertial weight balance algorithm,the global and local search capabilities are balanced.Five benchmark functions are selected to test the performance of the algorithm,and the experimental results verify the effectiveness of the proposed algorithm in optimizing performance.

关 键 词：群智能算法 蜉蝣优化算法 LOGISTIC映射 扰动 动态惯性权重 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]