Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (10): 296-307.doi: 10.11896/jsjkx.241000025

• Artificial Intelligence • Previous Articles     Next Articles

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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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

CLC Number: 

  • TP391
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