Behnamian, J. (2018). Diversified Particle Swarm Optimization for Hybrid Flowshop Scheduling. Journal of Optimization in Industrial Engineering, (), -. doi: 10.22094/joie.2018.671.1433

Javad Behnamian. "Diversified Particle Swarm Optimization for Hybrid Flowshop Scheduling". Journal of Optimization in Industrial Engineering, , , 2018, -. doi: 10.22094/joie.2018.671.1433

Behnamian, J. (2018). 'Diversified Particle Swarm Optimization for Hybrid Flowshop Scheduling', Journal of Optimization in Industrial Engineering, (), pp. -. doi: 10.22094/joie.2018.671.1433

Behnamian, J. Diversified Particle Swarm Optimization for Hybrid Flowshop Scheduling. Journal of Optimization in Industrial Engineering, 2018; (): -. doi: 10.22094/joie.2018.671.1433

Diversified Particle Swarm Optimization for Hybrid Flowshop Scheduling

Articles in Press, Accepted Manuscript , Available Online from 14 February 2018

^{}Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran

Abstract

The aim of this paper is to propose a new particle swarm optimization algorithm to solve a hybrid flowshop scheduling with sequence-dependent setup times problem, which is of great importance in the industrial context. This algorithm is called diversified particle swarm optimization algorithm which is a generalization of particle swarm optimization algorithm and inspired by an anarchic society whose members behave anarchically to improve their situations. Such anarchy lets the algorithm explore the solution space perfectly and prevent falling in the local optimum traps. Besides, for the first time, for the hybrid flowshop, we proposed eight different local search algorithms and incorporate them into the algorithm in order to improve it with the help of systematic changes of the neighborhood structure within a search for minimizing the makespan. The proposed algorithm was tested and the numerical results showe that the proposed algorithm significantly outperforms other effective heuristics recently developed.

Graphical Abstract

Highlights

This paper introduces diversified particle swarm optimization with local search.

DPSO is inspired by a society whose members behave anarchically.

In DPSO, the particles fickleness increases as their situations become worse.

The performance of the DPSO is examined on sequence-dependent hybrid flowshop.

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