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

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-obj...

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Veröffentlicht in:Computers & operations research Jg. 40; H. 1; S. 282 - 302
Hauptverfasser: Li, Xiang, Du, Gang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.01.2013
Elsevier
Pergamon Press Inc
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Atrie-tree
Binary search method
Boundaries
Boundary simulation method
Computer science; control theory; systems
Computer simulation
Constrained multi-objective optimization
Constraint handling
Constraints
Decision theory. Utility theory
Exact sciences and technology
Genetic algorithms
Inequality constraint
Mathematical models
Mathematical programming
Multi-objective optimization
Multiple criteria decision making
Operational research and scientific management
Operational research. Management science
Operations research
Optimization
Optimization algorithms
Pareto front
Pareto optimality
Pareto optimum
Pareto set
Population diversity
Rtrie-tree
Simulation
Studies
Theoretical computing
Trie-tree
Constraint handling
Boundary simulation method
Pareto front
Trie-tree
Pareto optimum
Binary search method
Genetic algorithms
Atrie-tree
Inequality constraint
Constrained multi-objective optimization
Rtrie-tree
Multi-objective optimization
Pareto set
Population diversity
Tree data structures
Multiobjective programming
Constraint satisfaction
Constrained optimization
Binary search tree
Genetic algorithm
Prefix tree
Mathematical programming
ISSN:0305-0548, 1873-765X, 0305-0548
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Beschreibung
Zusammenfassung: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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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2012.07.014