Abstract
The Particle Swarm Optimization (PSO) Algorithm attempts on the use of an improved range for inertia weight, social, and cognitive factors utilizing the Pareto principle. The function exhibits better convergence and search efficiency than PSO algorithms that use conventional linearly varying or exponentially varying inertia weights. It also presents a technique to intelligently navigate the search space around the obtained optima and looks for better optima if available and continue converging with the new values using a velocity restriction factor based on the Pareto principle. The improvised algorithm searches the neighborhood of the global optima while maintaining frequent resets in the position of some particles in the form of a mutation based on its escape probability. The results have been compared and tabulated against popular PSO with conventional weights and it has been shown that the introduced PSO performs much better on various benchmark functions.
References
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, Perth, Australia 4, 1942–1948 (1995)
Kennedy, J., Eberhart, R.C., Shi, Y.H.: Swarm Intelligence. Morgan Kaufmann, San Mateo, CA (2001)
Eberhart , R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of 6th International Symposium Micromachine Human Science, Nagoya, Japan, pp. 39–43 (1995)
Eberhart, R.C., Shi, Y.H.: Particle swarm optimization: developments, applications and resources. In: Proceedings of IEEE Congress on Evolutionary Computation, Seoul, Korea, pp. 81–86 (2001)
Ciuprina, G., Ioan, D., Munteanu, I.: Use of intelligent-particle swarm optimization in electromagnetics. IEEE Trans. Magn. 38(2), 1037–1040 (Mar 2002)
Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (Jun 2006)
Ho, S.-Y., Lin, H.-S., Liauh, W.-H., Ho, S.J.: OPSO: orthogonal particle swarm optimization and its application to task assignment problems. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 38(2), 288–298, Mar 2008
Liu, B., Wang, L., Jin, Y.H.: An effective PSO-based mimetic algorithm for flow shop scheduling. IEEE Trans. Syst. Man Cybern. B Cybern. 37(1), 18–27 (Feb 2007)
Eberhart, R.C., Shi, Y.: Guest editorial special issue particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 201–203 (Jun 2004)
Zhan, Z.-H., Zhang, J.: Adaptive particle swarm optimization. In: IEEE Trans. Syst. Man Cybern. B Cybern. 39(6), Dec 2009
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of IEEE World Congress Computation Intelligence, p. 6973 (1998)
Chen, T.-Y., Chi, T.-M.: On the improvements of the particle swarm optimization algorithm. Adv. Eng. Softw. 41, 229–239 (2010)
Bansal, J.C., Singh, P.K., Saraswat, M., Verma, A., Jadon, S.S., Abraham, A.: Inertia weight strategies in particle swarm optimization. Proceedings of IEEE International Conference on Neural Network, Perth, Australia 4, 1942–1948 (1995)
Das, S., Abrahamm, A., Konar, A.: Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspectives. Stud. Comput. Intell. (SCI) 116, 1–38 (2008)
Anand, B., Aakash, I., Akshay, Varrun, V., Reddy, M.K., Sathyasai, T., Devi, M.N.: Improvisation of particle swarm optimization algorithm. In: International Conference on Signal Processing and Integrated Networks (SPIN). India (2014)
Kiremire, A.R.: The application of pareto principle in software engineering. 19th October (2011)
Wikipedia. Pareto principle. http://en.wikipedia.org/wiki/paretoprinciple. Accessed March 2016
Virtual library of simulation experiments: test functions and datasets. http://www.sfu.ca/~ssurjano/. Accessed March 2016
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mouna, H., Mukhil Azhagan, M.S., Radhika, M.N., Mekaladevi, V., Nirmala Devi, M. (2018). Velocity Restriction-Based Improvised Particle Swarm Optimization Algorithm. In: Saeed, K., Chaki, N., Pati, B., Bakshi, S., Mohapatra, D. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 564. Springer, Singapore. https://doi.org/10.1007/978-981-10-6875-1_34
Download citation
DOI: https://doi.org/10.1007/978-981-10-6875-1_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6874-4
Online ISBN: 978-981-10-6875-1
eBook Packages: EngineeringEngineering (R0)