Computer Science ›› 2021, Vol. 48 ›› Issue (4): 1-13.doi: 10.11896/jsjkx.200600151

• Computer Science Theory • Previous Articles     Next Articles

Survey of Constrained Evolutionary Algorithms and Their Applications

LI Li, LI Guang-peng, CHANG Liang, GU Tian-long   

  1. Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
  • Received:2020-06-24 Revised:2020-09-26 Published:2021-04-09
  • About author:LI Li,born in 1986,Ph.D,M.S supervisor,is a member of China Computer Federation.His main research interests include multi-objective optimization methods and their applications.
  • Supported by:
    National Natural Science Foundation of China(62006058,U1811264,U1711263,61966009) and Guangxi Natural Science Foundation(2018GXNSFAA138090,2018GXNSFDA281049,2017GXNSFAA198283).

Abstract: Constrained optimization problems exist widely in scientific research and engineering practice,and the corresponding constrained evolutionary algorithms have become an important research direction in the field of evolutionary computation.The essential problem of constrained evolutionary algorithm is how to effectively use the information of infeasible and feasible solutions and balance the objective function and constraints to make the algorithm more efficient.Firstly,this paper defines the problem of constraint optimization.Then it analyzes the current mainstream constraint evolution algorithms in detail.At the same time,based on different constraint handling mechanisms,these mechanisms are divided into constraint and objective separation methods,pena-lty function methods,multi-objective optimization methods,hybrid methods and so on,and these methods are analyzed and summarized comprehensively.Next,it points out the urgent problems that need to be solved as well as the research direction.Finally,the application of constrained evolutionary algorithm in engineering optimization,electronic and communication engineering,mechanical design,environmental resource allocation,scientific research and management allocation are introduced.

Key words: Constraint handling mechanism, Constraint optimization evolutionary algorithm, Constraint optimization problem, Engineering practice, Evolutionary algorithm

CLC Number: 

  • TP181
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