An Artificial Bee Colony algorithm with guide of global & local optima and asynchronous scaling factors for numerical optimization |
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Affiliation: | 1. College of Science, China University of Petroleum, Beijing 102249, China;2. School of Information Science and Engineering, Central South University, Changsha 410083, China;3. Department of Mathematics and Statistics, Curtin University, Perth, WA 6845, Australia;1. Ankara University, Faculty of Science, Statistics Department, Tando?an, Ankara, Turkey;2. Mu?la S?tk? Koçman University, Faculty of Science, Statistics Department, Kötekli, Mu?la, Turkey;1. School of Computer Science and Engineering, Xi''an University of Technology, Xi''an 710048, China;2. Shaanxi Key Laboratory for Network Computing and Security Technology, Xi''an 710048, China |
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Abstract: | Artificial Bee Colony (ABC) algorithm is a wildly used optimization algorithm. However, ABC is excellent in exploration but poor in exploitation. To improve the convergence performance of ABC and establish a better searching mechanism for the global optimum, an improved ABC algorithm is proposed in this paper. Firstly, the proposed algorithm integrates the information of previous best solution into the search equation for employed bees and global best solution into the update equation for onlooker bees to improve the exploitation. Secondly, for a better balance between the exploration and exploitation of search, an S-type adaptive scaling factors are introduced in employed bees’ search equation. Furthermore, the searching policy of scout bees is modified. The scout bees need update food source in each cycle in order to increase diversity and stochasticity of the bees and mitigate stagnation problem. Finally, the improved algorithms is compared with other two improved ABCs and three recent algorithms on a set of classical benchmark functions. The experimental results show that the our proposed algorithm is effective and robust and outperform than other algorithms. |
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Keywords: | Artificial Bee Colony algorithm Population-based optimization Scaling factors Global optimization |
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