目标个数不规则变化的动态多目标优化算法  

Dynamic Multi-Objective Optimization Algorithm with Irregularly Varying Number of Objectives

作　　者：栗三一 刘爽 LI Sanyi;LIU Shuang(Zhengzhou University of Light Industry,Zhengzhou 450000,China)

机构地区：[1]郑州轻工业大学,郑州450000

出　　处：《计算机科学》2024年第S02期172-182,共11页Computer Science

基　　金：国家自然科学基金(62203402,62103378);河南省科技攻关项目(202102310284,32102321034)。

摘　　要：文中提出了一种基于混合策略的初始种群预测算法(A Hybrid Strategy Based Initial Population Rrediction Algorithm,HIPPA)来解决目标个数随时间不规则变化的动态多目标优化问题。HIPPA依据目标个数判断环境是否发生变化,根据不同的目标个数划分环境类型。在种群初始化阶段,初始种群由3种机制产生。首先,利用历史种群信息训练改进的神经网络算法,生成一部分初始种群。其次,改进的精英策略利用历史种群信息生成一部分初始种群。最后,使用改进的随机策略生成一部分种群,以保持种群的多样性。本文使用基准实验F1-F5验证所提算法的有效性,并将结果与其他动态优化算法对比。实验结果表明,HIPPA可以更加有效地解决目标个数随时间不规则变化的动态多目标优化问题。In this paper,a hybrid strategy based initial population prediction algorithm(HIPPA)is proposed to solve the dynamic multi-objective optimization problem where the number of objectives varies irregularly with time.HIPPA determines whether the environment has changed according to the number of objectives,and divides the environment type according to the different number of objectives.In the population initialization stage,the initial population is generated by three mechanisms.First,an improved neural network algorithm is trained using historical population information to generate a part of the initial population.Second,the improved elite strategy uses historical population information to generate a portion of the initial population.Finally,an improved random strategy is used to generate a portion of the population to maintain the diversity of the population.In this paper,the effectiveness of the proposed algorithm is verified by reference experiment F1-F5,and the results are compared with other dynamic optimization algorithms.Experimental results show that HIPPA can more effectively solve the dynamic multi-objective optimization problem where the number of objectives varies irregularly with time.

关 键 词：动态多目标优化 神经网络 预测 目标个数不规则变化 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]