New Multi-Objective Constrained Optimization Evolutionary Algorithm

In this paper, a new evolutionary algorithm (EA) to solve multi-objective constrained optimization problem (MCOP) is proposed. First, the rank of the individual and the scalar constraint violation of the individual are defined. Then, based on the rank and the scalar constraint violation of the indiv...

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Veröffentlicht in:2008 3rd International Conference on Innovative Computing Information and Control S. 320
1. Verfasser: Chun-An Liu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2008
Schlagworte:
Distance measurement
Evolutionary computation
Genetic algorithms
Optimization
Probability
Simulation
Switches
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Zusammenfassung:In this paper, a new evolutionary algorithm (EA) to solve multi-objective constrained optimization problem (MCOP) is proposed. First, the rank of the individual and the scalar constraint violation of the individual are defined. Then, based on the rank and the scalar constraint violation of the individual, a new fitness function and a switch selection operator are presented. Accordingly, when the individuals are evaluated or ranked, it doesn't need to care about the feasibility of individuals, therefore it is a penalty-parameterless constraint-handling approach for multi-objective constrained optimization problem. Finally, the computer simulations demonstrate the effectiveness of the proposed algorithm.
DOI:10.1109/ICICIC.2008.387