Vol.5 No.1 (February 2015)
A Hybrid EDA with GA for the Permutation Flow Shop Scheduling Problem
The permutation flow shop scheduling problem is a classical combinatorial optimization in indus-trial engineering. Population-based evolutionary algorithms (EA) are the common methods to solve this problem. As a novel EA, estimation of distribution algorithm (EDA) directs the algorithm search towards good solutions by statistical learning. However, this algorithm may trap into the local optimal and lead to the premature convergence. To overcome the drawback of EDA, this paper incorporates EDA with GA to address the PFSP. The participation rates of EDA and GA are adaptively regulated by fuzzy logic controller. The experiment results on the benchmarks validate the efficiency of the proposed algorithm.
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