School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.
Shanghai Key Laboratory of Computer Software Testing and Evaluating, Shanghai 201112, China.
Comput Intell Neurosci. 2022 Nov 22;2022:5231262. doi: 10.1155/2022/5231262. eCollection 2022.
With the increasing complexity of users' needs and increasing uncertainty of a single web service in big data environment, service composition becomes more and more difficult. In order to improve the solution accuracy and computing speed of the constrained optimization model, several improvements are raised on ant colony optimization (ACO) and its calculation strategy. We introduce beetle antenna search (BAS) strategy to avoid the danger of falling into local optimization, and a service composition method based on fusing beetle-ant colony optimization algorithm (Be-ACO) is proposed. The model first generates search subspace for ant colony through beetle antenna search strategy and optimization service set by traversing subspace based on ant colony algorithm. Continuously rely on beetle antenna search strategy to generate the next search subspace in global scope for ant colony to traverse and converge to the global optimal solution finally. The experimental results show that compared with the traditional optimization method, the proposed method improves combination optimization convergence performance and solution accuracy greatly.
在大数据环境下，随着用户需求的日益复杂和单个 Web 服务的不确定性的增加，服务组合变得越来越困难。为了提高约束优化模型的求解精度和计算速度，对蚁群算法（ACO）及其计算策略进行了几点改进。我们引入了甲壳虫触角搜索（BAS）策略来避免陷入局部优化的危险，并提出了一种基于融合甲壳虫-蚁群算法（Be-ACO）的服务组合方法。该模型首先通过甲壳虫触角搜索策略为蚁群生成搜索子空间，并基于蚁群算法遍历子空间来优化服务集。然后，不断依赖甲壳虫触角搜索策略在全局范围内生成下一个搜索子空间，让蚁群进行遍历并最终收敛到全局最优解。实验结果表明，与传统优化方法相比，所提出的方法大大提高了组合优化的收敛性能和求解精度。
