摘要
为解决绿色产品族配置设计中的多目标优化问题,基于兼顾绿色性能和产品族成本的绿色产品族配置设计数学模型,通过模拟狼群捕食行为和种群更新方式,提出面向绿色产品族配置设计优化问题的改进离散狼群算法(IDWPA)。针对绿色产品族配置设计问题的组合优化特点,对狼群智能行为进行离散化操作,设计不可行解的修复和淘汰机制,引入带精英策略的非支配排序方法对人工狼进行分类和更新。以电动剪刀产品族为例,应用改进离散狼群算法和非支配排序遗传算法(NSGA-Ⅱ)对配置优化问题进行求解和比较,验证所提算法的有效性。结果表明:所提算法在解集质量表现方面更优,能有效平衡绿色性能与产品族总成本,并为绿色产品族配置设计提供可靠的优化工具。
In this article,in order to solve the multi-objective optimization problem in green product family configuration de-sign,a mathematic model of green product family configuration design is designed,which takes into account the green perform-ance and the cost of the product family;on this basis,IDWPA(the improved discrete wolf pack algorithm)is proposed for opti-mization of green product family configuration design by simulating the predation behavior of wolf packs and the population renewal mode.Aimed at the combinatorial optimization characteristics of green product family configuration design,IDWPA firstly oper-ates on the intelligent behavior of wolf packs in a discrete manner,designs the repair and elimination mechanism of infeasible so-lutions,and then introduces the non-dominated sorting method with the elite strategy to classify and update the artificial wolves.With the product family of electric scissors as an example,IDWPA and NSGA-Ⅱ(the non-dominated sorting genetic algorithmⅡ)are applied to solve and compare the configuration optimization problem,so as to verify that this algorithm is effective.It is concluded that the algorithm performs better in terms of solution set quality,effectively balances the green performance and the to-tal cost of the product family,and provides a reliable tool for optimization of green product family configuration design.
作者
赖荣燊
张航
高波
闫高强
LAI Rongshen;ZHANG Hang;GAO Bo;YAN Gaoqiang(School of Mechanical and Automotive Engineering,Xiamen University of Technology,Xiamen 361024)
出处
《机械设计》
北大核心
2025年第9期77-85,共9页
Journal of Machine Design
关键词
产品族
绿色性能
配置设计
改进离散狼群算法
非支配排序遗传算法
product family
green performance
configuration design
Improved Discrete Wolf Pack Algorithm
Non-domi-nated Sorting Genetic AlgorithmⅡ
