Abstract
Swarm intelligence algorithms are wildly used in different areas. The bare bones particle swarm optimization (BBPSO) is one of them. In the BBPSO, the next position of a particle is chosen from the Gaussian distribution. However, all particles learning from the only global best particle may cause the premature convergence and rapid diversity-losing. Thus, a BBPSO with dynamic local search (DLS-BBPSO) is proposed to solve these problems. Also, because the blind setting of local group may cause the time complexity an unpredictable increase, a dynamic strategy is used in the process of local group creation to avoid this situation. Moreover, to confirm the searching ability of the proposed algorithm, a set of well-known benchmark functions are used in the experiments. Both unimodal and multimodal functions are considered to enhance the persuasion of the test. Meanwhile, the BBPSO and several other evolutionary algorithms are used as the control group. At last, the results of the experiment confirm the searching ability of the proposed algorithm in the test functions.
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References
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)
Kennedy, J.: Bare bones particle swarms. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, SIS 2003, pp. 80–87. IEEE (2003)
Omran, M., Al-Sharhan, S.: Barebones particle swarm methods for unsupervised image classification. In: IEEE Congress on Evolutionary Computation, pp. 3247–3252. IEEE (2007)
Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(3), 204–210 (2004)
Kennedy, J., Mendes, R.: Neighborhood topologies in fully-informed and best-of-neighborhood particle swarms. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 36(4), 515–519 (2006)
Blackwell, T.: A study of collapse in bare bones particle swarm optimization. IEEE Trans. Evol. Comput. 16(3), 354–372 (2012)
Campos, M., Krohling, R.A., Enriquez, I.: Bare bones particle swarm optimization with scale matrix adaptation. IEEE Trans. Cybern. 44(9), 1567–1578 (2014)
Liang, J.J., Qin, A.K., Member, S., Suganthan, P.N., Member, S., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)
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Guo, J., Sato, Y. (2017). A Bare Bones Particle Swarm Optimization Algorithm with Dynamic Local Search. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_17
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DOI: https://doi.org/10.1007/978-3-319-61824-1_17
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