Abstract
As a kind of evolutionary algorithms, particle swarm optimization is famous for its simplicity and efficiency in optimization. However, for complex problems, PSO is prone to be trapped into the local optima. To address this issue, a particle swarm optimizer with niching strategy and entropy-based exploration strategy (PSO-NE) is proposed in this paper. To be specific, on one hand, a distance based niching strategy and the competitive learning strategy are adopted to design the exploitation in PSO-NE; on the other hand, the exploration in PSO-NE is achieved by an entropy based exploring strategy. With such kind of designs, the exploitation and exploration in PSO-NE can be dependently adjusted, which is beneficial for balancing these two factors. To validate the effectiveness of the proposed algorithm, extensive experiments have been conducted based on 28 benchmarks from CEC’ 2013. The proposed algorithm shows its competitive performance with comparing to six other typical variants of PSO.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: 6th Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43, IEEE, Nagoya (1995)
Eberhart, R., Kennedy, J.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE, Australia (1995)
Jin, N., Rahmat-Samii, Y.: Advances in particle swarm optimization for antenna designs: real-number, binary, single-objective and multiobjective implementations. IEEE Trans. Antennas Propag. 55(3), 556–567 (2007)
Ghamisi, P., Benediktsson, J.A.: Feature selection based on hybridization of genetic algorithm and particle swarm optimization. IEEE Geosci. Remote Sens. Lett. 12(2), 309–313 (2015)
Das, P.K., Behera, H.S., Panigrahi, B.K.: A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm Evol. Comput. 28, 14–28 (2016)
Koad, R.B.A., Zobaa, A.F., El-Shahat, A.: A novel MPPT algorithm based on particle swarm optimization for photovoltaic systems. IEEE Trans. Sustain. Energy 8(2), 468–476 (2017)
Zheng, Y., Ma, L., Zhang, L., et al.: Empirical study of particle swarm optimizer with an increasing inertia weight. In: The 2003 Congress on Evolutionary Computation, vol. 1, pp. 221–226. IEEE, Canberra, ACT, Australia (2003)
Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans. Evol. Comput. 8(3), 240–255 (2004)
Chatterjee, A., Siarry, P.: Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput. Oper. Res. 33(3), 859–871 (2006)
Zhan, Z.H., Zhang, J., Li, Y., et al.: Adaptive particle swarm optimization. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(6), 1362–1381 (2009)
Taherkhani, M., Safabakhsh, R.: A novel stability-based adaptive inertia weight for particle swarm optimization. Appl. Soft Comput. 38, 281–295 (2016)
Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(3), 204–210 (2004)
Liang, J.J., Qin, A.K., Suganthan, P.N., et al.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)
Cheng, R., Sun, C., Jin, Y.: A multi-swarm evolutionary framework based on a feedback mechanism. In: 2013 IEEE Congress on Evolutionary Computation, pp. 718–724. IEEE, Cancun (2001)
Cheng, R., Jin, Y.: A social learning particle swarm optimization algorithm for scalable optimization. Inf. Sci. 291, 43–60 (2015)
Yang, Q., Chen, W.N., Da Deng, J., et al.: A level-based learning swarm optimizer for large-scale optimization. IEEE Trans. Evol. Comput. 22(4), 578–594 (2018)
Cheng, R., Jin, Y.: A competitive swarm optimizer for large scale optimization. IEEE Trans. Cybern. 45(2), 191–204 (2015)
Chen, Y., Li, L., Xiao, J., et al.: Particle swarm optimizer with crossover operation. Eng. Appl. Artif. Intell. 70, 159–169 (2018)
Tang, R.L., Wu, Z., Fang, Y.J.: Adaptive multi-context cooperatively coevolving particle swarm optimization for large-scale problems. Soft. Comput. 21(16), 4735–4754 (2017)
Chen, W.N., Zhang, J., Lin, Y., et al.: Particle swarm optimization with an aging leader and challengers. IEEE Trans. Evol. Comput. 17(2), 241–258 (2013)
Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, vol. 3, pp. 1945–1950. IEEE, Washington (1999)
Shi, Y., Eberhart, R.C.: Fuzzy adaptive particle swarm optimization. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 101–106. IEEE, South Korea (2001)
van den Bergh, F., Engelbrecht, A.: A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 225–239 (2004)
den Bergh, F.V.: An analysis of particle swarm optimizers. Ph.D. dissertation, Dept. Comput. Sci., Univ. Pretoria, Pretoria, South Africa (2002)
Li, X., Yao, X.: Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans. Evol. Comput. 16(2), 210–224 (2012)
Qin, Q., Cheng, S., Zhang, Q., et al.: Particle swarm optimization with interswarm interactive learning strategy. IEEE Trans. Cybern. 46(10), 2238–2251 (2016)
Gong, Y.J., Li, J.J., Zhou, Y., et al.: Genetic Learning Particle Swarm Optimization. IEEE Trans. Cybern. 46(10), 2277–2290 (2017)
Bonyadi, M.R., Michalewicz, Z.: Particle swarm optimization for single objective continuous space problems: a review. Evol. Comput. 25(1), 1–54 (2017)
Sheng, W., Swift, S., Zhang, L., Liu, X.: A weighted sum validity function for clustering with a hybrid niching genetic algorithm. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 35(6), 1156–1167 (2005)
Wang, Y.N., Wu, L.H., Yuan, X.F.: Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure. Soft. Comput. 14(3), 193–209 (2010)
Liang, J.J., Qu, B.Y., Suganthan, P.N., et al.: Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report, 201212(34): 281–295 (2013)
Acknowledgments
This work was sponsored by the National Natural Science Foundation of China under Grant no. 71771176 and 61503287.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, D., Guo, W., Wang, L. (2019). Niching Particle Swarm Optimizer with Entropy-Based Exploration Strategy for Global Optimization. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11655. Springer, Cham. https://doi.org/10.1007/978-3-030-26369-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-26369-0_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26368-3
Online ISBN: 978-3-030-26369-0
eBook Packages: Computer ScienceComputer Science (R0)