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Multi-objective optimization with improved genetic algorithm

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4 Author(s)
Ishibashi, H. ; Fac. of Eng., Shinshu Univ., Nagano, Japan ; Aguirre, Hernan E. ; Tanaka, Kiyoshi ; Sugimura, T.

We extend an improved GA (GA-SRM) to the multi-objective flowshop scheduling problem (FSP) in order to obtain better pareto-optimum solutions (POS). Two kinds of cooperative-competitive genetic operators in GA-SRM, CM and SRM, are extended to ones suitable for FSP in which solutions (individuals) are represented as permutations. Simulation results verify that GA-SRM shows better performance for the multi-objective optimization problem (MOP), and consequently better POS are obtained than conventional approaches with canonical GA

Published in:

Systems, Man, and Cybernetics, 2000 IEEE International Conference on  (Volume:5 )

Date of Conference:

2000