A Novel Evolutionary Algorithm for Dynamic Constrained Multiobjective Optimization Problems

被引:86
作者
Chen, Qingda [1 ]
Ding, Jinliang [1 ]
Yang, Shengxiang [1 ,2 ]
Chai, Tianyou [1 ]
机构
[1] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Peoples R China
[2] De Montfort Univ, Sch Comp Sci & Informat, Ctr Computat Intelligence, Leicester LE1 9BH, Leics, England
基金
中国国家自然科学基金;
关键词
Optical fibers; Sociology; Statistics; Heuristic algorithms; Linear programming; Optimization; Convergence; Change response; dynamic constrained multiobjective optimization; population selection; test problems; GENETIC ALGORITHMS; STRATEGY; ENVIRONMENTS; IMMIGRANTS; OBJECTIVES; TIME;
D O I
10.1109/TEVC.2019.2958075
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
To promote research on dynamic constrained multiobjective optimization, we first propose a group of generic test problems with challenging characteristics, including different modes of the true Pareto front (e.g., convexity-concavity and connectedness-disconnectedness) and the changing feasible region. Subsequently, motivated by the challenges presented by dynamism and constraints, we design a dynamic constrained multiobjective optimization algorithm with a nondominated solution selection operator, a mating selection strategy, a population selection operator, a change detection method, and a change response strategy. The designed nondominated solution selection operator can obtain a nondominated population with diversity when the environment changes. The mating selection strategy and population selection operator can adaptively handle infeasible solutions. If a change is detected, the proposed change response strategy reuses some portion of the old solutions in combination with randomly generated solutions to reinitialize the population, and a steady-state update method is designed to improve the retained previous solutions. The experimental results show that the proposed test problems can be used to clearly distinguish the performance of algorithms, and that the proposed algorithm is very competitive for solving dynamic constrained multiobjective optimization problems in comparison with state-of-the-art algorithms.
引用
收藏
页码:792 / 806
页数:15
相关论文
共 64 条
[1]   Multiobjective optimization: When objectives exhibit non-uniform latencies [J].
Allmendinger, Richard ;
Handl, Julia ;
Knowles, Joshua .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2015, 243 (02) :497-513
[2]   On Handling Ephemeral Resource Constraints in Evolutionary Search [J].
Allmendinger, Richard ;
Knowles, Joshua .
EVOLUTIONARY COMPUTATION, 2013, 21 (03) :497-531
[3]  
Angantyr A, 2003, IEEE C EVOL COMPUTAT, P1560
[4]  
[Anonymous], 2005, COMPUTATIONAL INTELL
[5]   A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization [J].
Asafuddoula, M. ;
Ray, Tapabrata ;
Sarker, Ruhul .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2015, 19 (03) :445-460
[6]  
Azzouz R., 2016, P ANN C GEN EV COMP, P615
[7]   Handling time-varying constraints and objectives in dynamic evolutionary multi-objective optimization [J].
Azzouz, Radhia ;
Bechikh, Slim ;
Ben Said, Lamjed ;
Trabelsi, Walid .
SWARM AND EVOLUTIONARY COMPUTATION, 2018, 39 :222-248
[8]   A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy [J].
Azzouz, Radhia ;
Bechikh, Slim ;
Ben Said, Lamjed .
SOFT COMPUTING, 2017, 21 (04) :885-906
[9]  
Biswas S, 2014, 2014 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), P3192, DOI 10.1109/CEC.2014.6900487
[10]  
Branke J., 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), P1875, DOI 10.1109/CEC.1999.785502