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《Computer Engineering and Applications》 2007-24
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Dynamic evaluated immune Particle Swarm Optimization for Job-shop scheduling

CHANG Gui-juan1,2,ZHANG Ji-hui1 1.Complexity Science Institute of Qingdao University,Qingdao,Shandong 266071,China 2.The College of Science of LaiYang Agricultural University,Qingdao,Shandong 266109,China  
Traditional Particle Swarm Optimization(PSO) has some limitation to solve the combinatorial optimization problems.An Improved Particle Swarm Optimization(IPSO) by improving the iterative formula is proposed after analyzing the optimization mechanism of the PSO.In IPSO,to update the particles,the crossover idea of genetic algorithm is utilized by particles to exchange information.To keep excellent particle in the course of evolution,the optimization operator of acceleration is proposed and utilized.Particles are evaluated dynamically by immune algorithm in the course of evolution in order to avoid getting into the local search.The experimental results show that JSP Can be solved by IPSO effectively.The rationality of IPSO is validated.
【Fund】: 国家自然科学基金(the National Natural Science Foundation of China under Grant No.70671057);; 教育部博士点基金(No.20051065002);; 青岛市自然科学基金(the Natural Science Foundation of Qingdao City of China under Grant No.03-2-jz-19)
【CateGory Index】: TP301.6
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