摘要
服务优化组合旨在满足用户需求的前提下找到原子服务的最佳组合。针对目前求解服务优化组合问题效率低、寻优质量低的问题,提出了一种基于改进社会学习优化算法的多目标服务优化组合方法(ISLO-MOSCO)。结合多目标服务优化组合问题的特点,设计了服务组合实数编码模型,对社会学习优化算法的关键操作算子提出了改进,引入了Sigmoid扰动学习因子;结合改进的算法,提出了一种面向多目标服务优化组合问题的求解方法;大量实验验证了所提方法求解多目标服务优化组合问题的有效性与优越性。
Service composition optimization aims to find the best combination of atomic services that meet user requirements.A multi-objective service composition optimization method based on an improved social learning optimization algorithm(ISLO-MOSCO)is proposed to address the current problems of low efficiency and low seeking quality in solving service composition optimization problems.Firstly,combining the characteristics of the multi-objective service composition optimization problem,a service composition real number encoding model is designed,and the key operation operator of the social learning optimization algorithm is proposed to be improved by introducing a Sigmoid perturbation learning factor.Secondly,a solution method for multi-objective service composition optimization problems is proposed in combination with the improved algorithm.Finally,the effectiveness and superiority of the proposed method for solving multi-objective service composition optimization problems are verified by extensive experiments.
作者
海燕
徐芯
刘志中
HAI Yan;XU Xin;LIU Zhizhong(College of Information Engineering,North China University of Water Resources and Electric Power,Zhengzhou 450046,China;College of Computer and Control Engineering,Yantai University,Yantai Shandong 264005,China)
出处
《佳木斯大学学报（自然科学版）》
CAS
2023年第6期1-5,共5页
Journal of Jiamusi University：Natural Science Edition
基金
国家自然科学基金资助项目(61872126,62273290)
山东省自然科学基金重点项目(ZR2020KF019)。
关键词
服务组合
多目标优化
社会学习优化算法
QOS
service composition
multi-objective optimization
social learning optimization algorithm
QoS
