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Erschienen in:
2016 | OriginalPaper | Buchkapitel
A Hybrid Particle Swarm Optimization Algorithm for Solving Job Shop Scheduling Problems
verfasst von : Qiaofeng Meng, Linxuan Zhang, Yushun Fan
Erschienen in: Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems
Verlag: Springer Singapore
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Abstract
This paper proposes a new hybrid PSO optimization algorithm, which fuses GA and simulated annealing (SA) into the PSO algorithm. The crossover and mutation mechanism of GA algorithm make the new hybrid algorithm keep the diversity of population and retain the good factors in the population to jump out of local optimum. The sudden jump probability of SA also guarantees the diversity of the population, thus preventing local minimum of the hybrid PSO algorithm. This new hybrid algorithm is used to minimize the maximum completion time of the scheduling problems. The simulation results show that the performance of hybrid optimization algorithm outperforms another hybrid PSO algorithm. The hybrid PSO algorithm is not only in the structure of the algorithm, but also the search mechanism provides a powerful way to solve JSSP.