DocumentCode :
2838119
Title :
A hybrid optimization algorithm for multi-objective flexible job-shop
Author :
Xiu-Li, Zhang ; Yue, Huang ; Nian, Liu
Author_Institution :
Sch. of Inf. Sci. & Eng., Shenyang Ligong Univ., Shenyang, China
fYear :
2010
fDate :
26-28 May 2010
Firstpage :
1524
Lastpage :
1530
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.
Keywords :
job shop scheduling; learning (artificial intelligence); particle swarm optimisation; DPSO-HA algorithm; FJSP problem; double-swarm particle swarm optimization; global optimum solution; heuristic assignation; hybrid optimization; immune algorithm; learning strategy; multiobjective flexible job-shop scheduling; reproduction strategy; Heuristic algorithms; Information science; Particle swarm optimization; Scheduling algorithm; Double-swarm; Flexible job-shop scheduling; Learning strategy; Multi-objective; Particle swarm optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location :
Xuzhou
Print_ISBN :
978-1-4244-5181-4
Electronic_ISBN :
978-1-4244-5182-1
Type :
conf
DOI :
10.1109/CCDC.2010.5498261
Filename :
5498261
Link To Document :
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