Abstract
Particle swarm optimization (PSO) has gained significant attention for solving numerical optimization problems in different applications. However, the performance of PSO depends on the appropriate setting of inertia weight and the optimal setting changes with generations during the evolution. Therefore, different adaptive inertia weight strategies have been proposed. However, the best inertia weight adaptive strategy depends on the nature of the optimization problem. In this paper, different inertia weight strategies such as linear, Gompertz, logarithmic and exponential decreasing inertia weights as well as chaotic and oscillating inertia weight strategies are explored. Finally, PSO with an adaptive ensemble of linear & Gompertz decreasing inertia weights is proposed and compared with other strategies on a diverse set of benchmark optimization problems with different dimensions. Additionally, the proposed method is incorporated into heterogeneous comprehensive learning PSO (HCLPSO) to demonstrate its effectiveness.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: 4th IEEE International Conference on Neural Networks (1995)
Gao, Y., Duan, Y.: An adaptive particle swarm optimization algorithm with new random inertia weight. In: International Conference on Intelligent Computing, pp. 342–350 (2007)
Xin, J., Chen, G., Hai, Y..: A particle swarm optimizer with multi-stage linearly-decreasing inertia weight. In: International Joint Conference on Computational Sciences and Optimization, pp. 505–508 (2009)
Lynn, N., Suganthan, P.N.: Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol. Comput. 24, 11–24 (2015)
Nepomuceno, F.V., Engelbrecht, A.P.: A self-adaptive heterogeneous pso for real-parameter optimization. In: IEEE Congress on Evolutionary Computation (CEC), pp. 361–368 (2013)
van Zyl, E., Engelbrecht, A.: Comparison of self-adaptive particle swarm optimizers. In: IEEE Symposium on Swarm Intelligence (SIS), pp. 1–9 (2014)
Harrison, K.R., Engelbrecht, A.P., Ombuki-Berman, B.M.: The sad state of self-adaptive particle swarm optimizers. In: IEEE Congress on Evolutionary Computation (CEC), pp. 431–439 (2016)
Engelbrecht, A.P.: Heterogeneous particle swarm optimization. In: Dorigo, M., et al. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 191–202. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15461-4_17
Harrison, K.R., Engelbrecht, A.P., Ombuki-Berman, B.M.: Inertia weight control strategies for particle swarm optimization. Swarm Intell. 10, 267–305 (2016)
Jiang, M., Luo, Y., Yang, S.: Stagnation analysis in particle swarm optimization. In: Proceedings of the 2007 IEEE Swarm Intelligence Symposium (2007)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE World Congress on Computational Intelligence, pp. 69–73 (1998)
Bansal, J.C., Singh, P., Saraswat, M., Verma, A., Jadon, S.S., Abraham, A.: Inertia weight strategies in particle swarm optimization. In: Third World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 633–640 (2011)
Acknowledgment
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology under the Grant NRF- 2015R1C1A1A01055669.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Shirazi, M.Z., Pamulapati, T., Mallipeddi, R., Veluvolu, K.C. (2017). Particle Swarm Optimization with Ensemble of Inertia Weight Strategies. 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_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-61824-1_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61823-4
Online ISBN: 978-3-319-61824-1
eBook Packages: Computer ScienceComputer Science (R0)