A New Multi-objective Fully-Informed Particle Swarm Algorithm for Flexible Job-Shop Scheduling Problems

Author

Jia, Zhao-Hong ; Chen, Hua-Ping ; Tang, Jun

fYear

2007

fDate

15-19 Dec. 2007

Firstpage

191

Lastpage

194

Abstract

A novel Pareto-based multi-objective fully-informed particle swarm algorithm (FIPS) is proposed to solve flexible job-shop problems in this paper. Firstly, the population is ranked based on Pareto optimal concept. And the neighborhood topology used in FIPS is based on the Pareto rank. Secondly, the crowding distance of individuals is computed in the same Pareto level for the secondary rank. Thirdly, addressing the problem of trapping into the local optimal, the mutation operators based on the coding mechanism are introduced into our algorithm. Finally, the performance of the proposed algorithm is demonstrated by applying it to several benchmark instances and comparing the experimental results.

Keywords

Pareto optimisation; job shop scheduling; particle swarm optimisation; topology; Pareto rank; Pareto-based multiobjective fully-informed particle swarm algorithm; flexible job-shop scheduling; mutation operators; neighborhood topology; Competitive intelligence; Computational intelligence; Genetic mutations; Ground penetrating radar; Information management; Information security; Particle swarm optimization; Processor scheduling; Scheduling algorithm; Topology;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security Workshops, 2007. CISW 2007. International Conference on

Conference_Location

Heilongjiang

Print_ISBN

978-0-7695-3073-4

Type

conf

DOI

10.1109/CISW.2007.4425477

Filename

4425477