Abstract
Particle swarm optimization (PSO) is a stochastic swarm-based algorithm inspired by the intelligent collective behavior of some animals. There are very few parameters to adjust in PSO which makes PSO easy to implement. One of the important parameter is inertia weight (ω) which balances the exploration and exploitation properties of PSO in a search space. In this paper, a new variation of PSO has been proposed, which utilizes a novel adaptive inertia weight strategy based on the binomial probability distribution for global optimization. This new technique improves final accuracy and the convergence speed of PSO with better performance. This new strategy has been tested against a set of ten benchmark functions and compared with four other PSO variants. The result shows that this new strategy is better and very competitive in most of the cases than other PSO variants.
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
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, Perth, Australia (1995)
Eberhart, R.C., Shi, Y.: A modified particle swarm optimizer. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC’98), pp. 69–73, Anchorage, AK (1998)
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)
Kadirkamanathan, V., Selvarajah, K., Fleming, P.J.: Stability analysis of the particle dynamics in particle swarm optimizer. IEEE Trans. Evol. Comput. 10(3), 245–255 (2006)
Kennedy, J.: Bare bones particle swarms. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, 2003, SIS’03, pp. 80–87 (2003)
Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proceedings of the IEEE Congress, CEC 99, vol. 3, pp. 1945–1950, Washington, DC (1999)
Eberhart, R.C., Shi, Y.: Tracking and optimizing dynamic systems with particle swarms. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 1, pp. 94–100, Seoul, South Korea (2001)
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, vol. 1, pp. 505–508. IEEE, New York (2009)
Nikabadi, A., Ebadzadeh, M.: Particle swarm optimization algorithms with adaptive inertia weight: a survey of the state of the art and a novel method. IEEE J. Evol. Comput. (2008)
Nickabadi, A., Ebadzadeh, M.M., Safabakhsh, R.: A novel particle swarm optimization algorithm with adaptive inertia weight. Appl. Soft Comput. 11, 3658–3670 (2011)
Kessentini, S., Barchiesi, D.: Particle swarm optimization with adaptive inertia weight. Int. J. Mach. Learn. Comput. 5(5), 368–373 (2015)
Zhan, Z.-H., Zhang, J., Li, Y., Chung, H.S.-H.: Adaptive particle swarm optimization. IEEE Trans. Syst. Man Cybern.-Part B: Cybern. 39, 1362–1381 (2009)
Surjanovic, S., Bingham, D.: Virtual Library of Simulation Experiments: Test Functions and Datasets, http://www.sfu.ca/~ssurjano/ (2013)
Lei, K., Qiu, Y., He, Y.: A new adaptive well-chosen inertia weight strategy to automatically harmonize global and local search ability in particle swarm optimization. In: 1st IEEE International Symposium on Systems and Control in Aerospace and Astronautics, pp. 977–980, Harbin (2006)
Martínez, J.F., Gonzalo, E.G.: The PSO family: deduction, stochastic analysis and comparison. Swarm Intell. 3(4), 245–273 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Agrawal, A., Tripathi, S. (2019). Particle Swarm Optimization with Probabilistic Inertia Weight. In: Yadav, N., Yadav, A., Bansal, J., Deep, K., Kim, J. (eds) Harmony Search and Nature Inspired Optimization Algorithms. Advances in Intelligent Systems and Computing, vol 741. Springer, Singapore. https://doi.org/10.1007/978-981-13-0761-4_24
Download citation
DOI: https://doi.org/10.1007/978-981-13-0761-4_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0760-7
Online ISBN: 978-981-13-0761-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)