4.5 Article

Color Revolution: A Novel Operator for Imperialist Competitive Algorithm in Solving Cloud Computing Service Composition Problem

期刊

SYMMETRY-BASEL
卷 13, 期 2, 页码 -

出版社

MDPI
DOI: 10.3390/sym13020177

关键词

cloud computing; color revolution operator; imperialist competitive algorithm; quality of service; service composition; service time-cost

资金

  1. Ministry of Higher Education Malaysia [FRGS/1/2014/ICT07/UKM/02/1]
  2. Research University Grant [DIP-2018-041]
  3. Research Incentive Grant [GPP-2020-032]

向作者/读者索取更多资源

This paper proposes a novel operator for the imperialist competitive algorithm called the color revolution operator (CRO), which significantly improves the algorithm's efficiency. The study applies this improved algorithm to optimize service time-cost in cloud computing service composition, showing that ICACRO is more efficient than traditional algorithms.
In this paper, a novel high-performance and low-cost operator is proposed for the imperialist competitive algorithm (ICA). The operator, inspired by a sociopolitical movement called the color revolution that has recently arisen in some countries, is referred to as the color revolution operator (CRO). The improved ICA with CRO, denoted as ICACRO, is significantly more efficient than the ICA. On the other hand, cloud computing service composition is a high-dimensional optimization problem that has become more prominent in recent years due to the unprecedented increase in both the number of services in the service pool and the number of service providers. In this study, two different types of ICACRO, one that applies the CRO to all countries of the world (ICACRO-C) and one that applies the CRO solely to imperialist countries (ICACRO-I), were used for service time-cost optimization in cloud computing service composition. The ICACRO was evaluated using a large-scale dataset and five service time-cost optimization problems with different difficulty levels. Compared to the basic ICA and niching PSO, the experimental and statistical tests demonstrate that the ability of the ICACRO to approach an optimal solution is considerably higher and that the ICACRO can be considered an efficient and scalable approach. Furthermore, the ICACRO-C is stronger than the ICACRO-I in terms of the solution quality with respect to execution time. However, the differences are negligible when solving large-scale problems.

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