Abstract
Artificial bee colony (ABC) is an efficient global optimizer, which has bee successfully used to solve various optimization problems. However, most of these problems are low dimensional. In this paper, we propose a new multi-population ABC (MPABC) algorithm to challenge large-scale global optimization problems. In MPABC, the population is divided into three subpopulations, and each subpopulation uses different search strategies. During the search, all subpopulations exchange there best search experiences to help accelerate the search. Experimental study is conducted on ten global optimization functions with dimensions 50, 100, and 200. Results show that MPABC is better than three other ABC variants on all dimensions.
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Acknowledgement
This work was supported by the Science and Technology Plan Project of Jiangxi Provincial Education Department (No. GJJ170994), the National Natural Science Foundation of China (No. 61663028), the Distinguished Young Talents Plan of Jiangxi Province (No. 20171BCB23075), the Natural Science Foundation of Jiangxi Province (No. 20171BAB202035), and the Open Research Fund of Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing (No. 2016WICSIP015).
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Wang, H., Wang, W., Cui, Z. (2018). A New Artificial Bee Colony Algorithm for Solving Large-Scale Optimization Problems. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11335. Springer, Cham. https://doi.org/10.1007/978-3-030-05054-2_26
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DOI: https://doi.org/10.1007/978-3-030-05054-2_26
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