计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (10): 296-307.doi: 10.11896/jsjkx.241000025

• 人工智能 • 上一篇    下一篇

基于子问题有效性引导的多目标进化算法

孙良旭1, 李林林1, 刘国莉2   

  1. 1 辽宁科技大学计算机与软件工程学院 辽宁 鞍山 114051
    2 沈阳工业大学机械工程学院 沈阳 110300
  • 收稿日期:2024-10-08 修回日期:2025-01-27 出版日期:2025-10-15 发布日期:2025-10-14
  • 通讯作者: 孙良旭(sunliangxumail@163.com)
  • 基金资助:
    国家自然科学基金(61903169, 71301066);辽宁省教育厅项目(LJ212410142039)

Sub-problem Effectiveness Guided Multi-objective Evolution Algorithm

SUN Liangxu1, LI Linlin1, LIU Guoli2   

  1. 1 College of Computer and Software Engineering,University of Science and Technology Liaoning,Anshan,Liaoning 114051,China
    2 College of Mechanical Engineering,Shenyang University of Technology,Shenyang 110300,China
  • Received:2024-10-08 Revised:2025-01-27 Online:2025-10-15 Published:2025-10-14
  • About author:SUN Liangxu,born in 1979,Ph.D,associate professor.His main research in-terests include multi-objective evolutionary algorithm and production sche-duling.
  • Supported by:
    National Natural Science Foundation of China(61903169,71301066) and Liaoning Provincial Education Department Project(LJ212410142039).

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摘要： 为了解决基于分解多目标进化算法求解具有非常规Pareto前沿(Pareto Front,PF)的多目标优化问题(Multi-Objective Problems,MOPs)出现的性能变差、普适性不高等问题,提出了一种新的基于子问题有效性引导的多目标进化算法(Sub-problem Effectiveness Guided Multi-Objective Evolution Algorithm Based on Decomposition,MOEA/D-SEG)。算法扩展了子问题结构并描述权向量在进化过程的表现。通过裂变“高效”子问题实现权向量调整,使算法能够更好地适应不同特征的多目标优化问题,保证求得解集的收敛性和多样性,提高算法求解各类复杂多目标优化问题的能力。通过一系列实验,证明了提出算法在不同特征测试问题上的有效性。通过与其他先进算法进行比较分析,证明了提出算法的优越性。该算法在炼钢-连铸调度问题中的应用进一步验证了算法的实用性。

关键词: 多目标优化, 权向量, 子问题, 分解

Abstract: In order to solve the problems of poor performance and low universality in solving MOPs with unconventional PF based on decomposed multi-objective evolutionary algorithms,a new Multi-Objective Evolution Algorithm based on Decomposition and Sub-problem Effectiveness Guidance(MOEA/D-SEG) is proposed.The algorithm expands the sub-problem structure and describes the behaviour of weight vectors in the evolutionary process.The weight vector adjustment is realized by fission “efficient” sub-problems,so that the algorithm can better adapt to the multi-objective optimization problems with different characte-ristics,ensure the convergence and diversity of the solution set,and improve the ability of the algorithm to solve various complex multi-objective optimization problems.Through a series of experiments,the effectiveness of the proposed algorithm in different feature testing problems is proved,and the superiority of the proposed algorithm is proved by comparative analysis with other advanced algorithms.The application of the proposed algorithm in steel-making and continuous casting scheduling problem further verifies the feasibility.

Key words: Multi-objective optimization,Weight vector,Sub-problem,Decomposition

中图分类号: 

  • TP391
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