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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Bibliographic Details
Published in:Computers & operations research 2013-01, Vol.40 (1), p.282-302
Main Authors: Li, Xiang, Du, Gang
Format: Article
Language:English
Subjects:
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
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