IT-CEMOP: An iterative co-evolutionary algorithm for multiobjective optimization problem with nonlinear constraints

Over the past few years, researchers have developed a number of multiobjective evolutionary algorithms (MOEAs). Although most studies concentrate on solving unconstrained optimization problems, there exit a few studies where MOEAs have been extended to solve constrained optimization problems. Most o...

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Veröffentlicht in:Applied mathematics and computation Jg. 183; H. 1; S. 373 - 389
Hauptverfasser: Osman, M.S., Abo-Sinna, Mahmoud A., Mousa, A.A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY Elsevier Inc 01.12.2006
Elsevier
Schlagworte:
Calculus of variations and optimal control
Exact sciences and technology
Genetic algorithms
Mathematical analysis
Mathematics
Multiobjective evolutionary algorithms
Multiobjective nonlinear programming
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Sciences and techniques of general use
ε-Dominance
Multiobjective evolutionary algorithms
ε-Dominance
Multiobjective nonlinear programming
Genetic algorithms
Unconstrained optimization
Multiobjective algorithm
Evolutionary algorithm
Optimization method
Multiobjective programming
Iterative method
Non linear programming
e-Dominance
Constrained optimization
Penalty function
Genetic algorithm
Applied mathematics
Repair
Handling
Mathematical programming
ISSN:0096-3003, 1873-5649
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Zusammenfassung:Over the past few years, researchers have developed a number of multiobjective evolutionary algorithms (MOEAs). Although most studies concentrate on solving unconstrained optimization problems, there exit a few studies where MOEAs have been extended to solve constrained optimization problems. Most of them were based on penalty functions for handling nonlinear constraints by genetic algorithms. However the performance of these methods is highly problem-dependent, many methods require additional tuning of several parameters. In this paper, we present a new optimization algorithm, which is based on concept of co-evolution and repair algorithm for handling nonlinear constraints. The algorithm maintains a finite-sized archive of nondominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε-dominance. The use of ε-dominance also makes the algorithms practical by allowing a decision maker to control the resolution of the Pareto set approximation by choosing an appropriate ε value, which guarantees convergence and diversity. The results, provided by the proposed algorithm for six benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2006.05.095