On the importance of isolated infeasible solutions in the many-objective constrained NSGA-III

被引:14
作者
Elarbi, Maha [1 ]
Bechikh, Slim [1 ]
Ben Said, Lamjed [1 ]
机构
[1] Univ Tunis, Comp Sci Dept, SMART Lab, ISG Tunis, Tunis, Tunisia
关键词
Many-objective optimization; Constrained optimization; Evolutionary algorithms; Decomposition; NONDOMINATED SORTING APPROACH; EVOLUTIONARY ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; DECOMPOSITION; SEARCH; PERFORMANCE; SELECTION;
D O I
10.1016/j.knosys.2018.05.015
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recently, decomposition has gained a wide interest in solving multi-objective optimization problems involving more than three objectives also known as Many-objective Optimization Problems (MaOPs). In the last few years, there have been many proposals to use decomposition to solve unconstrained problems. However, fewer is the amount of works that has been devoted to propose new decomposition-based algorithms to solve constrained many-objective problems. In this paper, we propose the ISC-Pareto dominance (Isolated Solution-based Constrained Pareto dominance) relation that has the ability to: (1) handle constrained many-objective problems characterized by different types of difficulties and (2) favor the selection of not only infeasible solutions associated to isolated sub-regions but also infeasible solutions with smaller CV (Constraint Violation) values. Our constraint handling strategy has been integrated into the framework of the Constrained Non-Dominated Sorting Genetic Algorithm-III (C-NSGA-III) to produce a new algorithm called Isolated Solution-based Constrained NSGA-III (ISC-NSGA-III). The empirical results have demonstrated that our constraint handling strategy is able to provide better and competitive results when compared against three recently proposed constrained decomposition-based many-objective evolutionary algorithms in addition to a penalty-based version of NSGA-III on the CDTLZ benchmark problems involving up to fifteen objectives. Moreover, the efficacy of ISC-NSGA-III on a real world water management problem is showcased. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页数:22
相关论文
共 70 条
[1]  
Alam K., 2011, Proceedings 2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS 2011), P23, DOI 10.1109/CIVTS.2011.5949527
[2]  
[Anonymous], 2008, SPEC SESS PERF ASS M
[3]  
[Anonymous], 2010, 2010 UK WORKSH COMP
[4]  
[Anonymous], 2016, P 31 ANN ACM S APPL
[5]  
[Anonymous], 1994, COMPLEX SYST
[6]  
Asafuddoula M, 2012, IEEE C EVOL COMPUTAT
[7]   Steady State IBEA Assisted by MLP Neural Networks for Expensive Multi-Objective Optimization Problems [J].
Azzouz, Nessrine ;
Bechikh, Slim ;
Ben Said, Lamjed .
GECCO'14: PROCEEDINGS OF THE 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2014, :581-588
[8]  
Bechikh S, 2017, ADAPT LEARN OPTIM, V20, P105, DOI 10.1007/978-3-319-42978-6_4
[9]  
Bentley PJ, 1998, SOFT COMPUTING IN ENGINEERING DESIGN AND MANUFACTURING, P231
[10]  
Cheng R., 2016, IEEE T CYBERN