Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization

被引:453
作者
Li, Ke [1 ,2 ]
Chen, Renzhi [3 ]
Fu, Guangtao [4 ]
Yao, Xin [5 ,6 ]
机构
[1] Univ Elect Sci & Technol China, Coll Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China
[2] Univ Exeter, Dept Comp Sci, Exeter EX4 4QF, Devon, England
[3] Natl Univ Def Technol, Sch Comp Sci, Changsha 410073, Hunan, Peoples R China
[4] Univ Exeter, Dept Engn, Exeter EX4 4QF, Devon, England
[5] Univ Birmingham, Sch Comp Sci, CERCIA, Birmingham B15 2TT, W Midlands, England
[6] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen Key Lab Computat Intelligence, Shenzhen 518055, Peoples R China
基金
英国工程与自然科学研究理事会;
关键词
Constraint handling; evolutionary algorithm (EA); decomposition-based technique; multiobjective optimization; two-archive strategy; WATER DISTRIBUTION-SYSTEMS; PARADIGM; DESIGN;
D O I
10.1109/TEVC.2018.2855411
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
When solving constrained multiobjective optimization problems, an important issue is how to balance convergence, diversity, and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, a two-archive evolutionary algorithm, for constrained multiobjective optimization. It maintains two collaborative archives simultaneously: one, denoted as the convergence-oriented archive (CA), is the driving force to push the population toward the Pareto front; the other one, denoted as the diversity-oriented archive (DA), mainly tends to maintain the population diversity. In particular, to complement the behavior of the CA and provide as much diversified information as possible, the DA aims at exploring areas under-exploited by the CA including the infeasible regions. To leverage the complementary effects of both archives, we develop a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status. Comprehensive experiments on a series of benchmark problems and a real-world case study fully demonstrate the competitiveness of our proposed algorithm, in comparison to five state-of-the-art constrained evolutionary multiobjective optimizers.
引用
收藏
页码:303 / 315
页数:13
相关论文
共 45 条
[1]  
Angantyr A, 2003, IEEE C EVOL COMPUTAT, P1560
[2]  
[Anonymous], P CIMCA IAWTIC VIENN
[3]   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
[4]   The balance between proximity and diversity in multiobjective evolutionary algorithms [J].
Bosman, PAN ;
Thierens, D .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2003, 7 (02) :174-188
[5]   Dynamic Multiobjectives Optimization With a Changing Number of Objectives [J].
Chen, Renzhi ;
Li, Ke ;
Yao, Xin .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2018, 22 (01) :157-171
[6]   A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization [J].
Cheng, Ran ;
Jin, Yaochu ;
Olhofer, Markus ;
Sendhoff, Bernhard .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2016, 20 (05) :773-791
[7]   MOSES: A multiobjective optimization tool for engineering design [J].
Coello, CAC ;
Christiansen, AD .
ENGINEERING OPTIMIZATION, 1999, 31 (03) :337-368
[8]   A fast and elitist multiobjective genetic algorithm: NSGA-II [J].
Deb, K ;
Pratap, A ;
Agarwal, S ;
Meyarivan, T .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197
[9]  
Deb K., 1995, Complex Systems, V9, P115
[10]  
Deb K., 1996, A combined Genetic Adaptive Search (GeneAS) for Engineering Design, DOI DOI 10.1109/TEVC.2007.895269