Abstract
The bare bones particle swarm optimization (BBPSO) algorithm has been proved to be an effective tool for single-objective optimization problems over continuous search spaces. In this paper, a fission-fusion hybrid bare bones particle swarm optimizer (FHBBPSO) is proposed. The FHBBPSO combines a fission strategy and a fusion strategy to sample new positions of the particles. The fission strategy aims at splitting the search space. Particles are assigned to different local groups to sample the corresponding regions. On the other side, the fusion strategy aims at narrowing the search space. Marginal groups will be gradually merged by the central groups until there is only one group left. The two strategies work together for the theoretical optimal value. To confirm the searching ability of the FHBBPSO, the proposed method runs over the IEEE Congress on Evolutionary Computation 2014 (CEC2014) benchmark functions. Also, several famous evolutionary methods are used in the control group. The experimental results and analysis suggest that the FHBBPSO is a highly competitive optimization algorithm for single-objective functions.
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Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the international conference on neural networks, pp 1942–1948
Ghamisi P, Couceiro MS, Martins FM, Benediktsson JA (2014) Multilevel image segmentation based on fractional-order darwinian particle swarm optimization. IEEE Trans Geosci Remote Sens 52(5):2382–2394
Coelho LDS, Herrera BM (2007) Fuzzy identification based on a chaotic particle swarm optimization approach applied to a nonlinear yo-yo motion system. IEEE Trans Ind Electron 54(6):3234–3245
Gao Y., Zhang G., Lu J., Wee H. M. (2011) Particle swarm optimization for bi-level pricing problems in supply chains. J Glob Optim 51(2):245–254
Kennedy J. (2003) Bare bones particle swarms. In: Proceeding of the IEEE swarm intelligence symposium, pp 80–87
Koshti A., Arya L. D., Choube S. C. (2013) Voltage stability constrained distributed generation planning using modified bare bones particle swarm optimization. J Inst Eng India Ser B 94 (2):123–133. https://doi.org/10.1007/s40031-013-0052-1
Jiang B., Wang N. (2014) Cooperative bare-bone particle swarm optimization for data clustering. Soft Comput 18(6):1079–1091
Storn R., Price K. (1997) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359
Sarkar S., Das S. (2013) Multilevel image thresholding based on 2D histogram and maximum Tsallis entropy—A differential evolution approach. IEEE Trans Image Process 22(12):4788–4797
Das S., Abraham A., Konar A. (2008) Automatic clustering using an improved differential evolution algorithm. IEEE Trans Syst Man, Cybern A, Syst Humans 38(1):218–237
Campos M., Krohling R. A., Enriquez I. (2014) Bare bones particle swarm optimization with scale matrix adaptation. IEEE Transactions on Cybernetics 44(9):1567–1578
Guo J., Sato Y. (2017) A pair-wise bare bones particle swarm optimization algorithm. In: Proceedings of the IEEE/ACIS 16th international conference on computer and information science, pp 353–358
Guo J., Sato Y. (2017) A hierarchical bare bones particle swarm optimization algorithm. In: Proceedings of the 2017 IEEE international conference on systems man, and cybernetics, pp 1936–1941
Guo J., Sato Y. (2017) A bare bones particle swarm optimization algorithm with dynamic local search. Lecture Notes in Computer Science book series 10385:158–165
Guo J., Sato Y. (2018) A dynamic allocation bare bones particle swarm optimization algorithm and its application. J. Artificial Life and Robotics, Springer 23(3):353–358
Wang H., Rahnamayan S., Sun H., Omran M. G. H. (2013) Gaussian bare-bones differential evolution. IEEE Transactions on Cybernetics 43(2):634–647
Cai Y., Wang J. (2013) Differential evolution with neighborhood and direction information for numerical optimization. IEEE Transactions on Cybernetics 43(6):2202–2215
Du W., Leung S. Y. S., Tang Y., Vasilakos A. V. (2016) Differential evolution with event-triggered impulsive control. IEEE Transactions on Cybernetics 47(1):244–257
Liang JJ, Qu BY, Suganthan PN, Chen Q (2014) Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization
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Guo, J., Sato, Y. A fission-fusion hybrid bare bones particle swarm optimization algorithm for single-objective optimization problems. Appl Intell 49, 3641–3651 (2019). https://doi.org/10.1007/s10489-019-01474-9
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DOI: https://doi.org/10.1007/s10489-019-01474-9