起重机主梁轻量优化精确建模与智能求解方法  

Precise Modeling and Intelligent Solution Method of Crane Main Girder Lightweight Optimization

作　　者：郜少波 程精涛[2] GAO Shao-bo;CHENG Jing-tao(Hebei Vocational College of Labour Relations,Hebei Shijiazhuang050002,China;The Engineering&Technical College of Chengdu University of Technology,Sichuan Leshan614007,China)

机构地区：[1]河北劳动关系职业学院,河北石家庄050002 [2]成都理工大学工程技术学院,四川乐山614007

出　　处：《机械设计与制造》2020年第8期131-135,139,共6页Machinery Design & Manufacture

基　　金：国家科技支撑计划资助项目(2015BAF06B06)。

摘　　要：为了减轻起重机主梁自重,提出了基于多进化行为粒子群算法的主梁轻量化设计方法。建立了主梁轻量化设计模型,使用罚函数将约束优化问题转化为非约束优化问题;以粒子群算法为基础,制定了向群体最优学习、向自身学习、向其他粒子学习等多渠道信息来源方法,提出了四种粒子进化方法,以不同进化方法的即时价值和未来价值为依据,构造了粒子选择不同进化行为的概率,从而提出了多进化行为粒子群算法的优化模型智能求解方法。经测试函数验证,对于低维和高维寻优问题,多进化行为粒子群算法均具有超强的寻优的能力;与企业生产主梁相比,多进化行为粒子群算法优化后主梁面积减少了10.86%,且经有限元分析可知,优化后主梁仍满足约束条件,负荷设计要求。In order to lighten crane main girder weight,girder lightweight design method based on multiple evolution behaviors particle swarm algorithm is proposed. Girder lightweight optimizing design model is built,and constraint optimization problem is translated to unconstraint optimization problem by penalty function. On the basis of particle swarm algorithm,multi-channel information source is given,for example,learning from global best,itself,and other particles. Four evolution methods are put forward,and choosing probability of different evolution behavior is calculated according to instant value and feature value,so that intelligent solution method of multiple evolution behaviors particle swarm algorithm is raised. Clarified by test function,for low and high dimension problem,multiple evolution behaviors particle swarm algorithm possesses strong search ability.Compared with enterprise data,girder area decreases to 10.86% optimized by multiple evolution behaviors particle swarm algorithm. Analyzing optimized main girder by finite element,the girder still satisfies constraint condition,and meets the design requirements.

关 键 词：箱型主梁 多进化行为粒子群算法 多渠道信息来源 即时价值 未来价值 

分 类 号：TH16[机械工程—机械制造及自动化]