A New Artificial Bee Colony Algorithm for Solving Large-Scale Optimization Problems
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.
KeywordsArtificial bee colony Swarm intelligence Multi-population Global optimization Large-scale optimization
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).
- 3.Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
- 5.Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-TR06, Erciyes University, engineering Faculty, Computer Engineering Department (2005)Google Scholar
- 20.Tang, K., et al.: Benchmark functions for the CEC’2008 special session and competition on large scale global optimization. Nature Inspired Computation and Applications Laboratory, USTC, China (2007)Google Scholar
- 21.Herrera, F., Lozano, M., Molina, D.: Test suite for the special issue of Soft Computing on scalability of evolutionary algorithms and other metaheuristics for large scale continuous optimization problems. Technical report, University of Granada, Spain (2010)Google Scholar