BSTBGA: A hybrid genetic algorithm for constrained multi‐objective optimization problems

BSTBGA: A hybrid genetic algorithm for constrained multi‐objective optimization problems

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Article ID: iaor20125449
Volume: 40
Issue: 1
Start Page Number: 282
End Page Number: 302
Publication Date: Jan 2013
Journal: Computers and Operations Research
Authors: ,
Keywords: programming: multiple criteria, heuristics: genetic algorithms
Abstract:

Most of the existing multi‐objective genetic algorithms were developed for unconstrained problems, even though most real‐world problems are constrained. Based on the boundary simulation method and trie‐tree data structure, this paper proposes a hybrid genetic algorithm to solve constrained multi‐objective optimization problems (CMOPs). To validate our approach, a series of constrained multi‐objective optimization problems are examined, and we compare the test results with those of the well‐known NSGA‐II algorithm, which is representative of the state of the art in this area. The numerical experiments indicate that the proposed method can clearly simulate the Pareto front for the problems under consideration.

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