西安电子科技大学学报（自然科学版）2024，Vol.51Issue(3)：124-135,12.DOI:10.19665/j.issn1001-2400.20230902
一种新的基于预测的动态多目标进化算法
New prediction strategy based evolutionary algorithm for dynamic multi-objective optimization
摘要
Abstract
Dynamic multi-objective optimization problems(DMOPs)where the environments change over time require that an evolutionary algorithm be able to continuously track the moving Pareto set or Pareto front.Response strategies based prediction has received much attention.However,these strategies mostly use historical environmental information for prediction,which will make the predicted results inaccurate.In this paper,we strengthen the mining and utilization of new environmental information and propose a new prediction strategy based evolutionary algorithm for dynamic multi-objective optimization(RAM),which includes mainly two core parts,namely,response mechanism and acceleration mechanism.The response mechanism reinitializes the population after the environmental changes,some individuals are generated by the prediction strategy,which is close to the new environmental PS to improve the optimization ability of this algorithm,and the remaining individuals are generated by the local search strategy to increase the population diversity.The acceleration mechanism is used in the static optimization process to accelerate the convergence speed of the RAM.Finally,the RAM is compared with other three advanced dynamic multi-objective optimization algorithms on a series of test functions with different dynamic characteristics.The results show that the RAM has more advantages than other three algorithms in solving dynamic multi-objective optimization problems.关键词
进化算法/动态多目标优化/预测策略/新环境信息Key words
evolutionary algorithm/dynamic multi-objective optimization/prediction strategy/new environment information分类
数理科学引用本文复制引用
万梦依,武燕..一种新的基于预测的动态多目标进化算法[J].西安电子科技大学学报（自然科学版）,2024,51(3):124-135,12.基金项目
国家自然科学基金(62276202,62106186) （62276202,62106186）
陕西省自然科学基础研究计划项目(2022JQ-670) （2022JQ-670）
中央高校基本研究基金(QTZX22047) （QTZX22047）
