Abstract
Optimization of one or more objective function is a requirement for many real life problems. Due to their wide applicability in business, engineering and other areas, a number of algorithms have been proposed in literature to solve these problems to get optimal solutions in minimum possible time. Particle Swarm Optimization (PSO) is a very popular optimization algorithm, and was developed by Dr. James Kennedy and Dr. Russell Eberhart in 1995 which was inspired by social behavior of bird flocking or fish schooling. In order to improve the performance of PSO algorithm, number of its variants has been proposed in literature. Few variants such as PSO Bound have been designed differently, whereas others use various methods to tune the random parameters. PSO - Time Varying Inertia Weight (PSO-TVIW), PSO Random Inertia Weight (PSO-RANDIW), and PSO-Time Varying Acceleration Coefficients (PSO-TVAC), APSO-VI, LGSCPSOA and many more are based on parameter tuning. On similar principle, the proposed approach improves the performance of PSO algorithm by adding new parameter henceforth called as “acceleration to particle” in its velocity equation. Efficiency of the proposed algorithm is checked against other existing PSO, and results obtained are very encouraging.
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References
Kenndy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Premalatha, K., Natarajan, A.M.: Discrete PSO with GA operators for document clustering. Int. J. Recent Trends Eng. 1(1), 20–24 (2009)
Parsopoulos, K.E., Vrahatis, M.N.: Recent approaches to global optimization problems through particle swarm optimization. Nat. Comput. 1(2–3), 235–306 (2002)
Laskari, E.C., Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization for integer programming. In: WCCI, pp. 1582–1587. IEEE, May 2002
Van Den Bergh, F.: An analysis of particle swarm optimizers (Doctoral dissertation, University of Pretoria) (2006)
Brits, R., Engelbrecht, A.P., Van den Bergh, F.: A niching particle swarm optimizer. In: Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning, vol. 2, pp. 692–696. Orchid Country Club, Singapore, November 2002
http://en.wikipedia.org/wiki/ Particle swarm optimization
Zhang, J., Huang, D.S., Lok, T.M., Lyu, M.R.: A novel adaptive sequential niche technique for multimodal function optimization. Neurocomputing 69(16), 2396–2401 (2006)
Bird, S., Li, X.: Adaptively choosing niching parameters in a PSO. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 3–10. ACM, July 2006
Evers, G.I., Ben Ghalia, M.: Regrouping particle swarm optimization: a new global optimization algorithm with improved performance consistency across benchmarks. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2009, pp. 3901–3908. IEEE, October 2009
Yao, J., Han, D.: Improved barebones particle swarm optimization with neighborhood search and its application on ship design. Math. Probl. Eng. 2013, Article ID 175848, 12 (2013). http://dx.doi.org/10.1155/2013/175848
Riget, J., Vesterstrøm, J.S.: A diversity-guided particle swarm optimizer-the ARPSO. Dept. Comput. Sci., Univ. of Aarhus, Aarhus, Denmark, Technical report 2 (2002)
Ye, F., Chen, C.Y.: Alternative KPSO-clustering algorithm. Tamkang J. Sci. Eng. 8(2), 165 (2005)
Barrera Alviar, J., Peña, J., Hincapié, R.: Subpopulation best rotation: a modification on PSO. Revista Facultad de Ingeniería Universidad de Antioquia (40), pp. 118–122 (2007)
Zavala, A.E., Aguirre, A.H., Diharce, E.R.: Continuous constrained optimization with dynamic tolerance using the COPSO algorithm. In: Mezura-Montes, E. (ed.) Constraint-Handling in Evolutionary Optimization. SCI, vol. 198, pp. 1–23. Springer, Heidelberg (2009)
Yin, P.Y., Laguna, M., Zhu, J.X.: A complementary cyber swarm algorithm (2011)
El-Abd, M., Kamel, M.S.: Particle swarm optimization with varying bounds. In: Evolutionary Computation, CEC 2007 (2007)
El-Abd, M., Kamel, MS.: Particle swarm optimization with adaptive bounds. In: Evolutionary Computation (CEC) (2012)
Lin, W., Lian, Z., Gu, X., Jiao, B.: A local and global search combined particle swarm optimization algorithm and its convergence analysis. Math. Probl. Eng. 2014, 11 (2014)
Xu, G.: An adaptive parameter tuning of particle swarm optimization algorithm. Appl. Math. Comput. 219(9), 4560–4569 (2013)
Tiwari, S., Mishra, K.K., Misra, A.K.: Test case generation for modified code using a variant of particle swarm optimization (PSO) algorithm. In: 2013 Tenth International Conference on Information Technology: New Generations (ITNG), pp. 363–368. IEEE (2013)
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Tiwari, S., Mishra, K.K., Singh, N., Rawal, N.R. (2016). A New Particle Acceleration-Based Particle Swarm Optimization Algorithm. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_31
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DOI: https://doi.org/10.1007/978-3-319-41000-5_31
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