Title :
A New Multi-objective Fully-Informed Particle Swarm Algorithm for Flexible Job-Shop Scheduling Problems
Author :
Jia, Zhao-Hong ; Chen, Hua-Ping ; Tang, Jun
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;
Conference_Titel :
Computational Intelligence and Security Workshops, 2007. CISW 2007. International Conference on
Conference_Location :
Heilongjiang
Print_ISBN :
978-0-7695-3073-4
DOI :
10.1109/CISW.2007.4425477
