一种基于改进蒲公英优化的云制造服务组合策略  

A cloud manufacturing service composition strategy based on improved dandelion optimizer algorithm

作　　者：董靖宇 郭银章[1] DONG Jingyu;GUO Yinzhang(Crowdsourcing and Cloud Computing Laboratory,Taiyuan University of Science and Technology,Taiyuan 030024,China)

机构地区：[1]太原科技大学群智计算与云计算实验室,太原030024

出　　处：《现代制造工程》2025年第4期25-35,共11页Modern Manufacturing Engineering

基　　金：山西省中央引导地方科技发展资金项目(YDZJSX20231A044)。

摘　　要：针对云制造服务组合存在的能力需求匹配度低、多约束条件下组合优化困难及寻优效率低下等问题,给出了一种基于改进蒲公英优化(Dandelion Optimizer,DO)算法的服务组合优化方法。在分析了云制造能力供应商以及服务质量属性的基础上,采用层次分析法(Analytic Hierarchy Process,AHP)将各个属性进行归一化求和,使用一种基于改进DO算法的服务组合方法对服务组合问题进行求解,得到最优的服务组合方案。在DO算法改进方面,通过引入Tent混沌映射来提高种群粒子的多样性,采用反向学习机制以及引入计数器和变异的概念提高了算法收敛速度,避免了算法过早收敛。最后通过仿真实验与经典蒲公英优化算法以及服务组合相关文献中提出的改进粒子群算法、改进遗传算法、改进北极熊算法等算法对比分析,验证了所提算法在云制造服务组合优化中高效性和稳定性。Given the low capability-demand alignment,the difficulty of optimizing combinations under multiple constraints,and the low efficiency in optimization,a service combination optimization method based on the improved Dandelion Optimizer(DO)algorithm is proposed for cloud manufacturing.After analyzing the capabilities of cloud manufacturing service providers and the quality attributes of services,the Analytic Hierarchy Process(AHP)is utilized to normalize and aggregate various attribu-tes.Subsequently,an improved DO algorithm is employed to solve the service combination problem and obtain the optimal service combination solution.In terms of improving the DO algorithm,diversity of population particles is enhanced by introducing the Tent chaotic mapping.Additionally,the convergence speed of the algorithm is improved by incorporating reverse learning mechanisms,counters,and mutation concepts,thus preventing premature convergence.Finally,through simulation experiments and comparative analysis with classical dandelion optimizer algorithm and other improved algorithms such as particle swarm optimization,genetic algorithm,and polar bear optimization proposed in relevant literature on service combination,the effectiveness and stability of the proposed algorithm in cloud manufacturing service combination optimization are verified.

关 键 词：云制造 服务组合 蒲公英优化算法 混沌映射 反向学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]