Improved Quantum-behaved Particle Swarm Optimization for Solving Multi-objective Flexible Job-Shop Scheduling Problems

Authors

Tian Na, 1. Institute of Educational Informatization, Jiangnan University, Wuxi 214122, China; ;2. Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China;
Zhicheng Ji, 2. Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: Due to the complexity of flexible job-shop scheduling problem (FJSP), it is still the hot topic for research. FJSP was given deep insight into with three objectives to be minimized simultaneously: makespan, maximal machine workload and total workload. Quantum-behaved particle swarm optimization (QPSO) with different coefficient selection methods was compared. The benchmark function tests show that QPSO with adaptive coefficient outperforms other selection methods in unimodal functions, while QPSO with cosine coefficient performs better in multi-modal functions. Therefore, QPSO with cosine decreasing coefficient is adopted to solve the multi-objective FJSP, which is a complex multi-modal optimization problem. Simulation results of four representative FJSP examples indicate the effectiveness and efficiency of the proposed method.

Recommended Citation

Na, Tian and Ji, Zhicheng (2020) "Improved Quantum-behaved Particle Swarm Optimization for Solving Multi-objective Flexible Job-Shop Scheduling Problems," Journal of System Simulation: Vol. 27: Iss. 12, Article 11.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss12/11

First Page

2948

Revised Date

2015-09-06

DOI Link

https://doi.org/

Last Page

2957

CLC

TP183

Recommended Citation

Tian Na, Ji Zhicheng. Improved Quantum-behaved Particle Swarm Optimization for Solving Multi-objective Flexible Job-Shop Scheduling Problems[J]. Journal of System Simulation, 2015, 27(12): 2948-2957.