基于多目标粒子群算法的动力吸振器参数优化和决策研究  

Multi-objective particle optimization and multi-attribute decision making study of dynamic vibration absorber

作　　者：王淳[1] 陈雅菊[1] 

机构地区：[1]武昌船舶重工有限责任公司,武汉湖北430060

出　　处：《舰船科学技术》2009年第11期46-50,共5页Ship Science and Technology

摘　　要：动力吸振器在船舶领域得到广泛应用。在船舶振动控制中需要寻找吸振器的最优参数,即最优频率比、最优阻尼比和最优质量比,使得结构在不同的频率激励下获得最好的减振效果。本文将基于多目标粒子群算法的优化技术与多属性决策方法联合运用,针对主系统存在阻尼的减振系统,研究了动力吸振器参数优化和决策问题。对于多目标优化问题,采用多目标粒子群算法(σ-MOPSO)求出Pareto最优解,基于熵方法得到属性权重,用逼近理想解的排序方法(TOPSIS)对Pareto最优解给出排序。文中给出了4个设计参数、2个目标函数的动力吸振器优化设计算例。计算结果表明,文中提出的联合方法能够有效应用于动力吸振器的参数优化。When dynamic vibration absorber（ DVA） is used in structural passive control,its optimal parameters, optimal frequency ratio, optimal damp ratio and optimal mass ratio should be computed to reduce the vibration of main system. A hybrid approach for multi-objective optimization study of DVA is proposed in present analysis. In the first stage, a multiobjective particle swarm optimization （sigma-MOPSO）is employed to approximate the set of Pareto solution through an evolutionary optimization process. In the subsequent stage, a multi-attribute decision making （MADM）approach is adopted to rank these solutions from best to worst and to determine the best solution in a deterministic environment with a single decision maker. A DVA example is conducted to illustrate the analysis process in present study. Pareto frontiers are obtained and the ranking of Pareto solution is based on entropy weight and TOPSIS method.

关 键 词：动力吸振器 多目标粒子群算法 多目标优化 多属性决策 TOPSIS 

分 类 号：O328[理学—一般力学与力学基础]