An effective hybrid optimization algorithm for the flow shop scheduling problem

Author

Kai, Sun ; Genke, Yang

Author_Institution

Dept. of Autom., Shanghai Jiao Tong Univ., Shanghai

fYear

2006

fDate

20-23 Aug. 2006

Firstpage

1234

Lastpage

1238

Abstract

This paper presents a new multi-swarm co-evolutionary algorithm named parallel particle swam optimization (PPSO) on the basis of standard PSO algorithm. Simulated annealing (SA) algorithm was introduced to increase escaping probability from local optima. By reasonably combining the PPSO with SA, we develop a general, fast and easily implemented hybrid optimization algorithm, and apply it to solve flow shop scheduling problem. Comparing results indicate that the new hybrid method is an effective and competitive approach for the flow shop scheduling problem.

Keywords

evolutionary computation; flow shop scheduling; parallel algorithms; particle swarm optimisation; probability; simulated annealing; flow shop scheduling problem; multiswarm coevolutionary algorithm; optimization algorithm; parallel particle swam optimization algorithm; probability; simulated annealing algorithm; Automation; Birds; Job shop scheduling; Optimization methods; Particle swarm optimization; Processor scheduling; Scheduling algorithm; Simulated annealing; Standards development; Sun; Flow shop scheduling problem; Hybrid optimization algorithm; Parallel particle swarm optimization; Simulated annealing;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Acquisition, 2006 IEEE International Conference on

Conference_Location

Shandong

Print_ISBN

1-4244-0528-9

Electronic_ISBN

1-4244-0529-7

Type

conf

DOI

10.1109/ICIA.2006.305925

Filename

4097858