Abstract
This paper presents an enhanced artificial bee colony (ABC) algorithm for solving the web service selection problem. The proposed algorithm searches the best possible combination of web services to satisfy user requirements. An adapted neighborhood selection and replacement process and a swapping process are used to improve the ABC behavior. Neighboring nodes are employed to enhance ABC performance by encouraging exploration in early iterations, where bees have no knowledge regarding the search space, and by encouraging exploitation in later iterations to exploit bee knowledge of the search space. The swapping process is used to enhance ABC performance by randomly swapping portions among the best two solutions randomly. The idea behind this swap is to exploit the characteristics of the best solutions to generate new solutions. We compared the proposed algorithm with other algorithms in terms of quality and execution time using 60 different datasets. These datasets have different numbers of tasks and web services. The results indicate that the proposed algorithm finds better solutions compared to other algorithms. In addition, the results' summarization on 60 datasets with 30 different executions shows that the proposed algorithm outperforms the threshold-based algorithm by 6% and the enhanced ABC by 3% in terms of solutions' quality.