基于改进多目标蜂群算法的Web服务组合优化方法
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摘要
为解决Web服务组合优化方法中的组合多样性和服务质量的问题,在人工蜂群算法上提出改进,通过在算法中引入反向学习算子、精英引导策略和组合变异策略等操作,使得种群个体有针对性地进行更新,在保证服务组合质量的前提下,提高了服务组合的多样性.结果表明,所提算法具有良好的算法收敛性和均匀性,同时在为Web服务组合优化方面,也取得了较好的优化效果,提高了寻优精度、解的质量和收敛速度.
To solve the problem of combinatorial diversity and service quality in Web service composition optimization methods, an improvement in artificial bee colony algorithm was proposed. Several methods such as reverse learning operator, elite guidance strategy, and combination mutation strategy were led into the algorithm,by which targeted information could be provided to update individuals. Furthermore,the diversity of service portfolios was enhanced on the premise of ensuring the quality of service portfolios. The experimental results indicated that the refined algorithm has fast convergence speed and good uniformity. Meanwhile,a better optimistic effect was also received for the optimization of Web service composition, and the search accuracy,solution quality and convergence speed were improved as well.
To solve the problem of combinatorial diversity and service quality in Web service composition optimization methods, an improvement in artificial bee colony algorithm was proposed. Several methods such as reverse learning operator, elite guidance strategy, and combination mutation strategy were led into the algorithm,by which targeted information could be provided to update individuals. Furthermore,the diversity of service portfolios was enhanced on the premise of ensuring the quality of service portfolios. The experimental results indicated that the refined algorithm has fast convergence speed and good uniformity. Meanwhile,a better optimistic effect was also received for the optimization of Web service composition, and the search accuracy,solution quality and convergence speed were improved as well.
引文
[1]倪晚成,刘连臣,吴澄. Web服务组合方法综述[J].计算机工程,2008,34(4):79-81.(Ni Wan-cheng,Liu Lian-chen,Wu Cheng. Survey on Web services composition methods[J]. Computer Engineering,2008, 34(4):78-81.)
[2] Jaeger M C,Muhl G,Golze S. QoS-aware composition of Web services:a look at selection algorithms[C]//Proceedings of International Conference of Web Services.Orlando, 2005:646-661.
[3] Karaboga D. An idea based on honey bee swarm for numerical optimization[R]. Technical Report-TR06.Kayseri:Erciyes University,2005.
[4]周清雷,陈明昭,张兵.多目标人工蜂群算法在服务组合优化中的应用[J].计算机应用研究,2012,29(10):3625-3628.(Zhou Qing-lei,Chen Ming-zhao,Zhang Bing. Multi-objective artificial bee colony algorithm applied in QoS-aware service composition optimization[J]. Application Research of Computers,2012,29(10):3625-3628.)
[5] Wang L,Zhou G,Xu Y,et al. An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling[J]. International Journal of Advanced Manufacturing Technology,2012,60:1111-1123.
[6] Li J Q,Pan Q K,Gao K Z. Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems[J]. International Journal of Advanced Manufacturing Technology,2011,55:1159-1169.
[7] Ardagna D,Pernici B. Adaptive service composition in flexible processes[M]. London:IEEE Press,2007.
[8] Cardoso J,Sheth A,Miller J,et al. Quality of service for workflows and Web service processes[J]. Journal of Web Semantics,2004,1(3):281-308.
[9] Huo Y,Zhuang Y,Gu J J,et al. Discrete Gbest-guided artificial bee colony algorithm for cloud service composition[J]. Applied Intelligence,2015, 42(4):661-678.
[10] Zhu G,Kwong S. Gbest-guided artificial bee colony algorithm for numerical function optimization[J]. Applied Mathematics&Computation,2010,217(7):3166-3173.
[11] Yi W,Gao L,Zhou Y,et al. Differential evolution algorithm with variable neighborhood search for hybrid flow shop scheduling problem[C]//International Conference on Computer Supported Cooperative Work in Design. Chengdu,2016:233-238.
[12] Storn R,Price K. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization,1997, 11(4):341-359.
[13] Al-Masri E,Mahmoud Q H. QoS-based discovery and ranking of web services[C]//International Conference on Computer Communications and Networks. Honolulu,2007:529-534.
[2] Jaeger M C,Muhl G,Golze S. QoS-aware composition of Web services:a look at selection algorithms[C]//Proceedings of International Conference of Web Services.Orlando, 2005:646-661.
[3] Karaboga D. An idea based on honey bee swarm for numerical optimization[R]. Technical Report-TR06.Kayseri:Erciyes University,2005.
[4]周清雷,陈明昭,张兵.多目标人工蜂群算法在服务组合优化中的应用[J].计算机应用研究,2012,29(10):3625-3628.(Zhou Qing-lei,Chen Ming-zhao,Zhang Bing. Multi-objective artificial bee colony algorithm applied in QoS-aware service composition optimization[J]. Application Research of Computers,2012,29(10):3625-3628.)
[5] Wang L,Zhou G,Xu Y,et al. An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling[J]. International Journal of Advanced Manufacturing Technology,2012,60:1111-1123.
[6] Li J Q,Pan Q K,Gao K Z. Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems[J]. International Journal of Advanced Manufacturing Technology,2011,55:1159-1169.
[7] Ardagna D,Pernici B. Adaptive service composition in flexible processes[M]. London:IEEE Press,2007.
[8] Cardoso J,Sheth A,Miller J,et al. Quality of service for workflows and Web service processes[J]. Journal of Web Semantics,2004,1(3):281-308.
[9] Huo Y,Zhuang Y,Gu J J,et al. Discrete Gbest-guided artificial bee colony algorithm for cloud service composition[J]. Applied Intelligence,2015, 42(4):661-678.
[10] Zhu G,Kwong S. Gbest-guided artificial bee colony algorithm for numerical function optimization[J]. Applied Mathematics&Computation,2010,217(7):3166-3173.
[11] Yi W,Gao L,Zhou Y,et al. Differential evolution algorithm with variable neighborhood search for hybrid flow shop scheduling problem[C]//International Conference on Computer Supported Cooperative Work in Design. Chengdu,2016:233-238.
[12] Storn R,Price K. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization,1997, 11(4):341-359.
[13] Al-Masri E,Mahmoud Q H. QoS-based discovery and ranking of web services[C]//International Conference on Computer Communications and Networks. Honolulu,2007:529-534.
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