A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization

A considerable number of constrained optimization evolutionary algorithms (COEAs) have been proposed due to increasing interest in solving constrained optimization problems (COPs) by evolutionary algorithms (EAs). In this paper, we first review existing COEAs. Then, a novel EA for constrained optimi...

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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 10; H. 6; S. 658 - 675
Hauptverfasser: Cai, Zixing, Wang, Yong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.12.2006
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Acceleration
Algorithm design and analysis
Algorithms
Applied sciences
Archiving
Artificial intelligence
Benchmark testing
Computer aided instruction
Computer science; control theory; systems
Constrained optimization
Constraint optimization
Constraints
evolutionary algorithm (EA)
Evolutionary algorithms
Evolutionary computation
Exact sciences and technology
Inequalities
Information science
Learning and adaptive systems
Mathematical analysis
Mathematical models
multiobjective optimization
nondominated individuals
Optimization
Robustness
Studies
Information system
Evolutionary algorithm
Multiobjective programming
evolutionary algorithm (EA)
Replacement
Modeling
Constrained optimization
multiobjective optimization
Value function
Inequality constraint
Equality constraint
Population dynamics
Standard deviation
Objective function
Mathematical programming
nondominated individuals
ISSN:1089-778X, 1941-0026
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Zusammenfassung:A considerable number of constrained optimization evolutionary algorithms (COEAs) have been proposed due to increasing interest in solving constrained optimization problems (COPs) by evolutionary algorithms (EAs). In this paper, we first review existing COEAs. Then, a novel EA for constrained optimization is presented. In the process of population evolution, our algorithm is based on multiobjective optimization techniques, i.e., an individual in the parent population may be replaced if it is dominated by a nondominated individual in the offspring population. In addition, three models of a population-based algorithm-generator and an infeasible solution archiving and replacement mechanism are introduced. Furthermore, the simplex crossover is used as a recombination operator to enrich the exploration and exploitation abilities of the approach proposed. The new approach is tested on 13 well-known benchmark functions, and the empirical evidence suggests that it is robust, efficient, and generic when handling linear/nonlinear equality/inequality constraints. Compared with some other state-of-the-art algorithms, our algorithm remarkably outperforms them in terms of the best, mean, and worst objective function values and the standard deviations. It is noteworthy that our algorithm does not require the transformation of equality constraints into inequality constraints
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2006.872344