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基于多目标粒子群算法的切削用量多决策优化研究

Multi-Criteria Decision Optimization of Cutting Parameters Based on Multi-Objective Particle Swarm

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【作者】 朱小平王涛

【Author】 ZHU Xiao-ping1,WANG Tao2(1.Zhejiang Communications Institute College of Mechanical and Electrical,Hangzhou 311112,China;2.Department of Mechanical and Electrical Control Engineering,West Branch of Zhejiang University of Technology,Quzhou Zhejiang 324000,China)

【机构】 浙江交通职业技术学院机电学院浙江工业大学浙西分校机电控制工程系

【摘要】 传统多目标优化问题通常是以加权或约束方式将其转换为单一目标,未能反映多目标间的复杂关系,不利于随时根据需求作出有效的决策。为了更合理地确定切削用量,采用多目标粒子群算法首先求得问题的pareto最优前沿,经过后期多准则决策得到满足不同要求下的最优方案。采用这种先寻优后决策的方法,能有效弱化先验知识不足的影响,较传统多目标优化方法更为实用有效。并经与多目标遗传算法比较,多目标粒子群算法具有更优良的性能。

【Abstract】 Traditional multi-objective optimization,through simply converting to a single goal,often fails to reflect the more complex relationship between goals,and decision-make effectively at any time on demand.For selecting optimum cutting parameters,MOPSO(Multi-Objective Particle Swarm)is applied to obtain the pareto optimum front,and then the best answer is got according to multi-criteria decision.The method,making decision after searching optimum solutions,is more applicable and effective and can weak designer’s transcendental information deficiency problem.Compared with MOGA(Multi-Objective Genetic Algorithm),MOPSO showed better performance.

  • 【文献出处】 组合机床与自动化加工技术 ,Modular Machine Tool & Automatic Manufacturing Technique , 编辑部邮箱 ,2010年03期
  • 【分类号】TP301.6
  • 【被引频次】26
  • 【下载频次】266
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