Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6): 297-304.doi: 10.11896/jsjkx.210500095

• Artificial Intelligence • Previous Articles     Next Articles

Hidden Preference-based Multi-objective Evolutionary Algorithm Based on Chebyshev Distance

SUN Gang1, WU Jiang-jiang1, CHEN Hao1, LI Jun1, XU Shi-yuan2   

  1. 1 College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,China
    2 School of Information Science and Technology,North China University of Technology,Beijing 100144,China
  • Received:2021-05-13 Revised:2021-09-04 Online:2022-06-15 Published:2022-06-08
  • About author:SUN Gang,born in 1988,postgraduate.His main research interests include multi-objective evolutionary algorithms and satellite range scheduling problems.
    CHEN Hao,born in 1982,Ph.D,professor.His main research interests include data mining,machine learning and evolutionary computation.
  • Supported by:
    National Natural Science Foundation of China(61806211,U19A2058) and Natural Science Foundation of Hunan Province(2020JJ4103).

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Abstract: As an important branch of multi-objective optimization,preference-based multi-objective evolutionary algorithms have been widely used in scientific researches and engineering practices,which have important research significance.In order to obtain the extreme solutions and the knee solution with the most compromised performance over each optimization objective in multi-objective optimization problems,a definition of knee solution based on Chebyshev distance and its geometric interpretation is presented.Based on the definition,a multi-objective evolutionary algorithm HP-NSGA-II aiming to search for the extreme solutions and the knee solution is proposed.The regional dynamic updating strategy of the proposed algorithm updates the target regions dynamically in each iteration,and finally converges to the target regions.The strategy of maintaining the balance between regions ensures the balance of the number of individuals in each region,so that the individuals could be distributed evenly in each region.Based on widely used test functions,sufficient experimental verification is carried out,and the experimental results indicate that HP-NSGA-II algorithm can achieve better performance in terms of convergence,regional balance and regional controllability in two-dimensional and three-dimensional test problems,and can accurately obtain the extreme solutions and knee solution.

Key words: Chebyshev distance, Evolutionary algorithm, Extreme solution, Knee solution, Multi-objective optimization, Preference

CLC Number: 

  • TP301.6
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