NDLI: A hybrid optimization algorithm for multi-objective flexible job-shop

Content Provider	IEEE Xplore Digital Library
Author	Zhang Xiu-li Huang Yue Liu Nian
Copyright Year	2010
Description	Author affiliation: School of Information Science and Engineering, Shenyang Ligong University, 110168, China (Zhang Xiu-li; Huang Yue; Liu Nian)
Abstract	A hybrid optimization algorithm based on double-swarm particle swarm optimization (DPSO) algorithm and heuristic assignation(HA) algorithm is proposed to solve multi-objective flexible job-shop scheduling(FJSP) problem in this paper. In DPSO algorithm, the population is divided into two sub-populations and they are evolved with the different learning strategy respectively. The information is exchanged between the two parts and sizes of two sub-populations are changed according to corresponding iteration when population is again divided randomly into two parts in the appointed iteration. The reproduction strategy based on density of immune algorithm is introduced into DPSO algorithm to maintain the multiplicity of particle. DPSO algorithm can effectively avoid the premature convergence problem and improves the ability of searching a global optimum solution. DPSO-HA algorithm is applied to a set of benchmark problem instances. The simulation results have shown the effectiveness and feasibility of DPSO-HA algorithm for the multi-objective flexible job-shop scheduling.
Starting Page	1524
Ending Page	1530
File Size	327643
Page Count	7
File Format	PDF
ISBN	9781424451814
DOI	10.1109/CCDC.2010.5498261
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Flexible job-shop scheduling Information science Heuristic algorithms Double-swarm Multi-objective Learning strategy Particle swarm optimization Scheduling algorithm
Content Type	Text
Resource Type	Article

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