Hybrid Quantum-behaved Particle Swarm Optimization Algorithm for Solving Mould Job Shop Scheduling Problem

Authors

Zhou Kai, Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China;
Wang Yan, Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China;
Zhicheng Ji, Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: Quantum-behaved particle swarm optimization has better performance compared with particle swarm optimization, but it still has the problem of getting trapped into local optimum with premature convergence. According to the above problem, a hybrid algorithm included quantum-behaved particle swarm optimization and bat algorithm was proposed. On the one hand, the random walk strategy of bat algorithm was used to avoid getting into local optimum, on the other hand, the speed changing of bats’ sound was learned to transform the factor of quantum-behaved particle swarm optimization. The proposed algorithm was tested on five benchmark functions and a mould job shop scheduling example, compared with PSO (Particle Swarm Optimization) and QPSO (Quantum-Behaved Particle Swarm Optimization). The simulated experimental results indicate the validity and superiority of the hybrid algorithm.

Recommended Citation

Kai, Zhou; Yan, Wang; and Ji, Zhicheng (2020) "Hybrid Quantum-behaved Particle Swarm Optimization Algorithm for Solving Mould Job Shop Scheduling Problem," Journal of System Simulation: Vol. 28: Iss. 6, Article 1.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss6/1

First Page

1247

Revised Date

2015-12-07

DOI Link

https://doi.org/

Last Page

1255

CLC

TP391.9

Recommended Citation

Zhou Kai, Wang Yan, Ji Zhicheng. Hybrid Quantum-behaved Particle Swarm Optimization Algorithm for Solving Mould Job Shop Scheduling Problem[J]. Journal of System Simulation, 2016, 28(6): 1247-1255.