A Hybrid 2-Population Genetic Algorithm for the Job Shop Scheduling Problem

Zhuo Xu; Rui Wang; Zhong Min Wang

doi:10.4028/www.scientific.net/AMR.989-994.2609

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994A Hybrid 2-Population Genetic Algorithm for the...

A Hybrid 2-Population Genetic Algorithm for the Job Shop Scheduling Problem

Abstract:

In this paper, an analysis of a hybrid two-population genetic algorithm (H2PGA) for the job shop scheduling problem is presented. H2PGA is composed of two populations that constitute of similar fit chromosomes. These two branches implement genetic operation separately using different evolutionary strategy and exchange excellent chromosomes using migration strategy which is acquired by experiments. Improved adaptive genetic algorithm (IAGA) and simulated annealing genetic algorithm (SAGA) are applied in two branches respectively. By integrating the advantages of two techniques, this algorithm has comparatively solved the two major problems with genetic algorithm which are low convergence velocity and potentially to be plunged into local optimum. Experimental results show that the H2PGA outperforms the other three methods for it has higher convergence velocity and higher efficiency.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

2609-2612

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.2609

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Online since:

July 2014

Authors:

Zhuo Xu, Rui Wang, Zhong Min Wang*

Keywords:

Genetic Algorithm (GA), Hybrid 2-Population GA, IAGA, Job Shop Scheduling, Saga

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* - Corresponding Author

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