Improved artificial bee colony algorithm based on self-adaptive random optimization strategy
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In order to effectively overcome the disadvantages of the traditional artificial bee colony (ABC) algorithm, i.e., its tendency to fall into local optima and low search speed, an improved ABC algorithm based on the self-adaptive random optimization strategy (SRABC) is proposed. First, the improved algorithm was derived from the self-adaptive method to update the new location of an ABC to improve the correlation within the bee colony. It converges swiftly and obtains the optimal solution for the benchmark function. Second, the bidirectional random optimization mechanism was used to restrain the search direction for the fitness function in order to improve the local search ability. Moreover, the particle swarm optimization algorithm regarded as the initial value of the SRABC algorithm was introduced at the initial stage of the improved ABC algorithm to increase the convergence rate, search precision and searchability, and greatly reduce the search space. Finally, simulation results for benchmark functions show that the proposed algorithm has obviously better performance regarding the search ability and convergence rate, which also prevents early maturing of algorithm.
KeywordsSwarm intelligence Artificial bee colony (ABC) Bidirectional random optimization (BRO) Self-adaptive Particle swarm optimization (PSO)
Part of the results in this paper appeared in the Proceedings of the 9th International Symposium on Computational Intelligence and Design (ISCID), 2016. This work is supported by the Scientific Research Program of the Higher Education Institution of Xinjiang under Grant No. XJEDU2016I049, the Natural Science Foundation of Xinjiang Uygur Autonomous Region under Grant No. 2017D01B09, Youth Research start-up fund project of School of Science and Technology Xinjiang Agricultural University under Grant No. 2016KJKY006 and No. 2016KJKY007.
- 2.Kennedy, J., Ebethart, R.: Particle swarm optimization. In: Proceeding of IEEE International Conference on Neural Networks. IEEE Computer Society, Piscataway, pp. 1942–1948 (1995)Google Scholar
- 3.Karaboga, D.: An idea based on honey bee swarm for numerical optimization, Technical Report-TR06. Computer Engineering Department, Engineering Faculty, Erciyes University (2005)Google Scholar
- 6.Abraham, A., Jatoth, R.K., Rajasekhar, A.: Hybrid differential artificial bee colony algorithm. J. Comput. Theor. Nanosci. 9(2), 1–9 (2013)Google Scholar
- 10.Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of IEEE World Congress on Evolutionary Computation, Anchorage, Alaska, USA, pp. 69–73, May 1998Google Scholar
- 12.Kishor, A., Chandra, M., Singh, P.K.: An astute artificial bee colony algorithm. J. Adv. Intell. Syst. Comput. 546, 153–162 (2017)Google Scholar
- 15.Huo, F., Liu, Y., Wang, D., et al.: Bloch quantum artificial bee colony algorithm and its application in image threshold segmentation. Signal Image Video Process. 11(12), 1–8 (2017)Google Scholar
- 20.Zhang, P., Li, J., Hu, X., et al.: Crossover-based artificial bee colony algorithm for constrained optimization problems. Neural Comput. Appl. 26(7), 1587–1601 (2017)Google Scholar
- 23.Wen Ming, M.A., Meng, X.W., Zhang, Y.J.: Bidirectional random walk search mechanism for unstructured P2P network. J. Softw. 23(4), 894–911 (2013)Google Scholar
- 24.Jia, Z., Si, X., Wang, T.: Optimum method for sea clutter parameter based on artificial bee colony. J. Cent. South Univ. (Sci. Technol.) 43(9), 3485–3489 (2012)Google Scholar
- 25.Sethi, D., Singhal, A.: Comparative analysis of a recommender system based on ant colony optimization and artificial bee colony optimization algorithms. In: International Conference on Computing, Communication and Networking Technologies, pp. 1–4. IEEE Computer Society (2017)Google Scholar
- 26.Zhao, Z., Huang, W.: Improved artificial bee swarm algorithm and its application in optimal operation of wind-power generators. J. Cent. South Univ. (Sci. Technol.) 42(10), 3101–3104 (2011)Google Scholar