Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 231000079-11.doi: 10.11896/jsjkx.231000079

• Intelligent Computing • Previous Articles     Next Articles

Dynamic Multi-Objective Optimization Algorithm with Irregularly Varying Number of Objectives

LI Sanyi, LIU Shuang   

  1. Zhengzhou University of Light Industry,Zhengzhou 450000,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:LI Sanyi,born in 1988,Ph.D,lecturer.His main research interests include multi-objective optimization,process automation in process industry,neural network modeling and fault diagnosis of power system equipment.
  • Supported by:
    National Natural Science Foundation of China(62203402,62103378) and Science and Technology Project of Henan Province(202102310284,232102321034).

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Abstract: tIn this paper,a hybrid strategy based initial population prediction algorithm(HIPPA) is proposed to solve the dynamic multi-objective optimization problem where the number of objectives varies irregularly with time.HIPPA determines whether the environment has changed according to the number of objectives,and divides the environment type according to the different number of objectives.In the population initialization stage,the initial population is generated by three mechanisms.First,an improved neural network algorithm is trained using historical population information to generate a part of the initial population.Second,the improved elite strategy uses historical population information to generate a portion of the initial population.Finally,an improved random strategy is used to generate a portion of the population to maintain the diversity of the population.In this paper,the effectiveness of the proposed algorithm is verified by reference experiment F1-F5,and the results are compared with other dynamic optimization algorithms.Experimental results show that HIPPA can more effectively solve the dynamic multi-objective optimization problem where the number of objectives varies irregularly with time.

Key words: Dynamic multi-objective optimization, Neural network, Prediction, The number of objectives varies irregularly

CLC Number: 

  • TP301
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