Solving Job-Shop Scheduling Problem by an Improved Genetic Algorithm

Yan Li Yang; Wei Wei Ke

doi:10.4028/www.scientific.net/AMR.411.588

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 411Solving Job-Shop Scheduling Problem by an Improved...

Solving Job-Shop Scheduling Problem by an Improved Genetic Algorithm

Abstract:

An improved genetic algorithm is proposed by introducing selection operation and crossover operation, which overcomes the limitations of the traditional genetic algorithm, avoids the local optimum, improves its convergence rate and the diversity of population, and solves the problems of population prematurity and slow convergence rate in the basic genetic algorithm. Simulation results show that compared with other improved genetic algorithms, the proposed algorithm is better in finding global optimal and convergent rate.

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Periodical:

Advanced Materials Research (Volume 411)

Pages:

588-591

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.411.588

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Online since:

November 2011

Authors:

Yan Li Yang, Wei Wei Ke

Keywords:

Clonal Selection Algorithm, Genetic Algorithm (GA), Job Shop Schedule Problem

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References

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