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
Online-Zugang:	Volltext
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