Journal
APPLIED SOFT COMPUTING
Volume 14, Issue -, Pages 363-380Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2013.10.008
Keywords
Constrained multi-objective optimization; Constraint handling; Modified objective function method; Feasible-guiding strategy
Categories
Funding
- National Natural Science Foundation of China [61001202, 61003199]
- China Post-Doctoral Science Foundation [201104658, 20090451369]
- National Research Foundation for the Doctoral Program of Higher Education of China [200807010003, 20100203120008, 20090203120016]
- Fund for Foreign Scholars in University Research and Teaching Programs [B07048]
- Program for Cheung Kong Scholars and Innovative Research Team in University [IRT1170]
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For constrained multi-objective optimization problems (CMOPs), how to preserve infeasible individuals and make use of them is a problem to be solved. In this case, a modified objective function method with feasible-guiding strategy on the basis of NSGA-II is proposed to handle CMOPs in this paper. The main idea of proposed algorithm is to modify the objective function values of an individual with its constraint violation values and true objective function values, of which a feasibility ratio fed back from current population is used to keep the balance, and then the feasible-guiding strategy is adopted to make use of preserved infeasible individuals. In this way, non-dominated solutions, obtained from proposed algorithm, show superiority on convergence and diversity of distribution, which can be confirmed by the comparison experiment results with other two CMOEAs on commonly used constrained test problems. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.
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