Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250500081-11.doi: 10.11896/jsjkx.250500081

• Artificial Intelligence • Previous Articles     Next Articles

Multi-objective Evolutionary Method by Training Front Modeling Based on MOEA/D

LI Li, YI Jiali, LI Youjun, LI Guangpeng   

  1. Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
  • Online:2026-06-16 Published:2026-06-12
  • About author:LI Li,born in 1986,Ph.D,associate professor,master's supervisor,is a member of CCF(No.39086M).His main research interest is multi-objective optimization methods and their applications.
  • Supported by:
    National Natural Science Foundation of China(62366009).

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Abstract: Multi-objective evolutionary algorithm based on decomposition(MOEA/D) is a widely employed optimization strategy in real-world applications.However,choosing a decomposition strategy of the MOEA/D that is not suitable for the curvature of Pareto Front(PF) can produce unsatisfactory results when dealing with multi-objective optimization problems.To address this issue,a multi-objective evolutionary algorithm based on MOEA/D by training PF models,named MOEA/D-ECM,is designed and adopted to solve the problem of the sensitivity of decomposition strategies to PF curvature.The algorithm trains a generic PF model to predict the curvature of the PF and then selects an appropriate decomposition strategy based on the predicted curvature.In addition,to ensure the diversity of the algorithm,a niche technique and distribution strategy is incorporated into the MOEA/D algorithm to select mating parents and improve the quality of the offspring.To evaluate the performance of this algorithm,several multi-objective evolutionary algorithms are compared on different test problems with concave,convex,and linear PF.The experimental results demonstrate that the MOEA/D-ECM algorithm can effectively solve multi-objective optimization problems for PF with different curvatures and has good performance and competitiveness.

Key words: PF model, Decomposition strategy, Multi-objective optimization problems, fitness function

CLC Number: 

  • TP181
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