一种新的基于预测的动态多目标进化算法

万梦依; 武燕

doi:10.19665/j.issn1001-2400.20230902

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一种新的基于预测的动态多目标进化算法

计算机科学与技术 & 人工智能 | 更新时间：2024-07-23

- 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
- 西安电子科技大学学报 2024年51卷第3期页码：124-135
- 作者机构：
  
  1. 西安电子科技大学数学与统计学院,陕西西安 710071
  2. 西安电子科技大学协同智能系统教育部重点实验室,陕西西安 710071
- 作者简介：
  
  [ "万梦依(1998—),女,西安电子科技大学硕士研究生,E-mail:[email protected]" ]
  武燕(1975—),女,副教授,E-mail:[email protected]
- 基金信息：
  
  国家自然科学基金(62276202);国家自然科学基金(62106186);陕西省自然科学基础研究计划项目(2022JQ-670);中央高校基本研究基金(QTZX22047)
- DOI：10.19665/j.issn1001-2400.20230902
  中图分类号： O29
- 纸质出版日期：2024-06-20，
  
  网络出版日期：2023-09-14，
  
  收稿日期：2023-03-03，
- 稿件说明：
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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, 2024,51(3):124-135.

Mengyi WAN, Yan WU. New prediction strategy based evolutionary algorithm for dynamic multi-objective optimization[J]. Journal of Xidian University, 2024,51(3):124-135.
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, 2024,51(3):124-135. DOI： 10.19665/j.issn1001-2400.20230902.

Mengyi WAN, Yan WU. New prediction strategy based evolutionary algorithm for dynamic multi-objective optimization[J]. Journal of Xidian University, 2024,51(3):124-135. DOI： 10.19665/j.issn1001-2400.20230902.

摘要

动态多目标优化问题(DMOPs)是指目标函数随时间变化的一类问题

算法求解的目标是持续跟踪移动的Pareto 最优解集或 Pareto最优前沿。基于预测的方法受到格外的关注

然而这些方法多使用历史环境信息进行预测

考虑到使用历史信息预测会存在预测不准确的问题

加强新环境信息的挖掘和利用

提出了一种新的基于预测的动态多目标进化算法

该算法主要包括两个核心部分

分别记为响应机制和加速机制。响应机制在环境变化后重新初始化群体

一部分的个体由预测策略产生

以生成靠近下一环境Pareto 最优解集的个体来提高算法的寻优能力

剩余部分个体采用局部搜索策略生成以增加种群多样性。加速机制用于静态优化过程以提高算法收敛速度。最后

将动态多目标进化算法与其他3种先进的动态多目标优化算法在具有不同动态特征的一系列测试函数上进行实验对比

结果表明

动态多目标进化算法相比其他3个算法在求解动态多目标优化问题中更具有优势。

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

Keywords

evolutionary algorithmdynamic multi-objective optimizationprediction strategynew environment information

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