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《Computer Integrated Manufacturing Systems》 2006-06
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General particle swarm optimization algorithm for job-shop scheduling problem

PENG Chuan-yong,GAO Liang,SHAO Xin-yu,ZHOU Chi(Sch.of Mechanical Sci.& Eng.,Huazhong Univ.of S & T,Wuhan 430074,China)  
To overcome the limitations of traditional Particle Swarm Optimization(PSO) on solutions to the combinatorial optimization problems,a General PSO(GPSO) model was proposed after analyzing the optimization mechanism of the traditional PSO. Based on this model,a GPSO algorithm was presented to solve the Job-shop Scheduling Problem(JSP).In GPSO,crossover and mutation operations in genetic algorithm were respectively utilized by particles to exchange information and search randomly.Besides,Tabu Search was used for particles' local search.To control the local search and ensure its convergence to the global optimum solution,time-varying crossover probability and time-varying maximum step size of Tabu Search were introduced.The experimental results showed that JSP could be solved by GPSO effectively.The feasibility of the proposed GPSO model was also demonstrated.
【Fund】: 国家自然科学基金资助项目(50305008)~~
【CateGory Index】: TP278
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