ABSTRACT

At present, with the emergence and development of cloud manufacturing (CMfg), the scale of services in CMfg platforms increases rapidly which provide the same or familiar functionality but different performance. Large-scale cloud service composition and optimization (CSCO) problems is one of the key issues for the implementation of CMfg. To deal with this NP-hard problem, a novel hybrid algorithm called Bee-Colony Simplex method hybrid Algorithm (ABCSA) for CSCO problems is proposed in this paper, which employs both the Simplex method and chaotic and global best guided strategy. The random-evolve Simplex method is proposed to maintain the algorithm work efficiently to keep the population diversity and avoid premature convergence. The global best guided and chaos searching strategy is proposed to avoid local optimization. To evaluate the effectiveness and efficiency, simulation and analysis of the experiments are carried out, and the results clearly prove the superior performance of ABCSA over existing intelligent optimization algorithms in the CSCO problems.

摘要

目前，随着云制造（CMfg）的出现和发展，CMfg平台中的服务规模快速增长，提供相同或熟悉的功能但性能不同。大规模云服务组合与优化（CSCO）问题是CMfg实施的关键问题之一。为了解决这个 NP-hard 问题，本文提出了一种新的混合算法，称为 Bee-Colony Simplex 方法混合算法（ABCSA），用于 CSCO 问题，它同时采用了 Simplex 方法和混沌和全局最佳引导策略。提出随机进化单纯形方法以保持算法有效工作，以保持种群多样性并避免早熟收敛。为了避免局部优化，提出了全局最佳引导和混沌搜索策略。