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Multi-Objective Optimal Approach for Injection Molding Based on Surrogate Model and Particle Swarm Optimization Algorithm

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【作者】 陈巍周雄辉王会凤王婉

【Author】 CHEN Wei1,ZHOU Xiong-hui1,WANG Hui-feng2,WANG Wan1(1.National Die and Mold CAD Engineering Research Center,Shanghai Jiaotong University,Shanghai 200030,China;2.School of Material Science and Engineering,University of Science and Technology Beijing,Beijing 100083,China)

【机构】 National Die and Mold CAD Engineering Research Center,Shanghai Jiaotong UniversitySchool of Material Science and Engineering,University of Science and Technology Beijing

【摘要】 An integrated optimization strategy based on Kriging model and multi-objective particle swarm optimization(PSO) algorithm was constructed.As a new surrogate model technology,Kriging model has better fitting precision for nonlinear problem.The Kriging model was adopted to replace computer aided engineering(CAE) simulation as fitness function of multi-objective PSO algorithm,and the computation cost can be reduced greatly.By introducing multi-objective handling mechanism of crowding distance and mutation operator to multiobjective PSO algorithm,the entire Pareto front can be approximated better.It is shown that the multi-objective optimization strategy can get higher solving accuracy and computation efficiency under small sample.

【Abstract】 An integrated optimization strategy based on Kriging model and multi-objective particle swarm optimization(PSO) algorithm was constructed.As a new surrogate model technology,Kriging model has better fitting precision for nonlinear problem.The Kriging model was adopted to replace computer aided engineering(CAE) simulation as fitness function of multi-objective PSO algorithm,and the computation cost can be reduced greatly.By introducing multi-objective handling mechanism of crowding distance and mutation operator to multiobjective PSO algorithm,the entire Pareto front can be approximated better.It is shown that the multi-objective optimization strategy can get higher solving accuracy and computation efficiency under small sample.

【基金】 the National Natural Science Foundation of China (No. 50873060)
  • 【文献出处】 Journal of Shanghai Jiaotong University(Science) ,上海交通大学学报(英文版) , 编辑部邮箱 ,2010年01期
  • 【分类号】TQ320.66
  • 【被引频次】7
  • 【下载频次】172
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