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《Computer Simulation》 2015-02
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Improved DPSO Based on Polychromatic Sets Theory and Research on Loading/Unloading Scheduling

YANG Wei;DANG Pei;FU WeiPing;QIU XiaoHong;Mechanical & Electrical Engineering College,Shaanxi University of Science& Technology;Faculty of Mechanical and Precision Instrument Engineering,Xi'an University of Technology;  
There are many factors affecting loading / unloading scheduling in Automated Storage and Retrieval System( AS / RS),so it is a complex problem. In order to avoid premature convergence of conventional genetic algorithm,an improved discrete particle swarm optimization algorithm based on polychromatic sets theory( PST) was presented. During the solution process,polychromatic sets matrices were used to assign loading / unloading goods location reasonably in the particle position of particle swarm optimization( PSO) to increase the primary particles quality,thus the search performance of the algorithm and optimize results can be improved. During the iterative process,parts of particles were reinitialized,so as to ensure the diversity of particles and avoid getting in local optimum. Compared with genetic algorithm and disperse particle swarm optimization( DPSO) through examples,the loading and unloading time is shorter,the convergence speed is faster and the iterative number is fewer,which verifies that the improved particle swarm optimization algorithm is feasible and efficacious in solving loading / unloading scheduling problem.
【Fund】: 国家自然科学基金资助项目(11072192);; 陕西科技大学科研启动基金项目(BJ12-21);; 陕西省农业科技创新与攻关项目(014K01-29-01);; 基于物联网的猪肉冷链物流追溯系统研究(14JK1093)
【CateGory Index】: TH692.3;TB497
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