Modified Differential Evolution Algorithm for Solving Dynamic Optimization with Existence of Infeasible Environments
作者机构：School of Engineering and Information Technology University of New South Wales CanberraAustralia
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2023年第74卷第1期
页 面：1-17页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the Australian Research Council Discovery Project(Grant Nos.DP210102939)
主 题：Dynamic optimization constrained optimization disruption differential evolution
摘 要：Dynamic constrained optimization is a challenging research topic in which the objective function and/or constraints change over *** such problems,it is commonly assumed that all problem instances are *** reality some instances can be infeasible due to various practical issues,such as a sudden change in resource requirements or a big change in the availability of ***-makers have to determine whether a particular instance is feasible or not,as infeasible instances cannot be solved as there are no solutions to *** this case,locating the nearest feasible solution would be valuable information for the *** this paper,a differential evolution algorithm is proposed for solving dynamic constrained problems that learns from past environments and transfers important knowledge from them to use in solving the current instance and includes a mechanism for suggesting a good feasible solution when an instance is *** judge the performance of the proposed algorithm,13 well-known dynamic test problems were *** results indicate that the proposed algorithm outperforms existing recent algorithms with a margin of 79.40%over all the environments and it can also find a good,but infeasible solution,when an instance is infeasible.
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