文献类型：期刊文献

中文题名：一种改进ε约束飞蛾火焰优化算法及其在约束优化问题中的应用

英文题名：An ε improved moth-flame optimization algorithm for solving constrained optimization problems and engineering applications

作者：叶文静[1];曹萃文[1];顾幸生[1]

机构：[1]华东理工大学能源化工过程智能制造教育部重点实验室,上海200237

年份：2023

卷号：38

期号：10

起止页码：2841

中文期刊名：控制与决策

外文期刊名：Control and Decision

收录：CSTPCD;;EI(收录号：20234014834544);Scopus;北大核心:【北大核心2020】；CSCD:【CSCD2023_2024】；

基金：国家自然科学基金项目(61673175,61973120)。

语种：中文

中文关键词：约束优化问题;飞蛾火焰优化算法;改进ε约束法;火焰更新;飞蛾变异策略

外文关键词：constrained optimization problem;moth-flame optimization algorithm;ε improved constraint method;flame update;moth mutation strategy

摘要：提出一种改进ε约束法的飞蛾火焰优化算法(ε improved moth-flame optimization algorithm,εIMFO)求解约束优化问题.该算法采用ε约束法对约束进行处理,考虑种群整体的约束违反度变化,提出一种基于火焰种群约束违反度的阈值ε计算公式;改进火焰种群的更新方法,首先,根据种群中个体的约束违反度与ε的关系将其分为两类:一类是约束违反度小于等于ε的个体,按照目标函数值排序,另一类是约束违反度大于ε的个体,按约束违反度排序;然后,先选择第1类中的个体,若数量没有达到种群数量的要求,继续从第2类中选取个体形成新一代火焰种群;最后,提出一种改进的飞蛾变异策略,在原始飞蛾变异策略的基础上引入2个随机火焰个体影响飞蛾变异,并增加优秀火焰个体对飞蛾变异的指导作用.通过25个测试函数以及2个实际的工程优化问题分别与其他13种算法进行的算法性能测试对比表明,εIMFO算法在求解精度和稳定性等方面具有优势.
An ε improved moth-flame optimization algorithm(εIMFO)is proposed to solve constrained optimization problems in this paper.Considering the change of the constraint violation degree of the population,this algorithm uses improved ε constraint method to deal with the constraints,and develops a threshold ε formula based on the constraint violation degree of the flame population.The update method of the flame population is improved.According to the relationship between the constraint violation degree of individuals in the population and ε,they are divided into two categories:one is the individuals whose constraint violation degree is less than or equal to ε,sorted according to the objective function value;the other is the individuals whose constraint violation degree is bigger than ε,sorted by constraint violation degree.Then the individuals in the first category are firstly selected.If the number does not meet the population size,individuals are selected from the second category to build a new flame population.Finally,an improved moth mutation strategy is proposed.Based on the original moth mutation strategy,two random flame individuals are introduced to affect moth mutation,and the guiding effect of the excellent flame individuals on moth mutation is increased.The comparison of algorithm performance tests with other 13 algorithms among 25 benchmark test functions and 2 actual engineering optimization problems shows that the ε IMFO algorithm is superior in terms of the accuracy and stability.