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 |
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| 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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| AbstractList | 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. [PUBLICATION 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. |
| Author | Li, Xiang Du, Gang |
| Author_xml | – sequence: 1 givenname: Xiang surname: Li fullname: Li, Xiang email: tju.som.li.xiang@gmail.com – sequence: 2 givenname: Gang surname: Du fullname: Du, Gang |
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| Keywords | 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 |
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| SubjectTerms | 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 |
| Title | BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems |
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