摘要
为解决产品设计中协作设计众包任务优化分配问题,建立了一个子任务对设计者匹配满意和设计者对子任务匹配满意的多目标优化模型,并提出一种改进的麻雀搜索算法进行求解。该模型以协作设计众包子任务和设计者双方最大匹配满意度为目标,从而建立双方的一对一匹配。对算法进行改进,利用Sinusoidal混沌映射初始化种群;利用正余弦算法指引所有麻雀个体向最优位置移动。为防止最优解陷入局部最优,对最优解加带惯性的柯西变异扰动;将改进的算法在6个基准测试函数上进行性能验证。试验结果表明:改进后的麻雀搜索算法寻优能力优于灰狼优化算法(GWO)、蝙蝠算法(BA)及麻雀搜索算法(SSA),并举出实例说明了方法的可行性。
In order to solve the optimal assignment issue of collaborative design crowdsourcing tasks in product design,a multi-objective optimization model was established where the subtask was satisfied with the designer’s matching and the designer was satisfied with the subtask’s matching. And then,an improved sparrow search algorithm was proposed to solve the issue. The model focused on the maximum matching satisfaction between the collaborative design crowdsourcing subtask and the designer.Thus,a one-to-one match between two parties was established. To improve the algorithm,Sinusoidal chaotic mapping was used to initialize the population. The sine cosine algorithm was used to guide all sparrow individuals to move to the optimal position. In order to prevent the optimal solution from falling into the local optimum,Cauchy variation with inertia was added into the optimal solution. The improved algorithm was verified on 6 benchmark test functions. The experiment results showed that the improved sparrow search algorithm has better performance than Gray Wolf Optimization Algorithm( GWO),Bat Algorithm( BA) and Sparrow Search Algorithm( SSA). Meanwhile,the example was given to illustrate feasibility of the method.
作者
刘电霆
吴丹玲
黄康政
LIU Dian-ting;WU Dan-ling;HUANG Kang-zheng(School of Mechanical and Control Engineering,Guilin University of Technology,Guilin 541004;School of Information Science and Engineering,Guilin University of Technology,Guilin 541004)
出处
《机械设计》
CSCD
北大核心
2021年第6期124-132,共9页
Journal of Machine Design
基金
国家自然科学基金资助项目(71961005)
广西自然科学基金资助项目(2020GXNSFAA297024)。
