Abstract
This paper proposes an adaptive particle swarm optimization (APSO) with adaptive parameters and elitist learning strategy (ELS) based on the evolutionary state estimation (ESE) approach. The ESE approach develops an ‘evolutionary factor’ by using the population distribution information and relative particle fitness information in each generation, and estimates the evolutionary state through a fuzzy classification method. According to the identified state and taking into account various effects of the algorithm-controlling parameters, adaptive control strategies are developed for the inertia weight and acceleration coefficients for faster convergence speed. Further, an adaptive ‘elitist learning strategy’ (ELS) is designed for the best particle to jump out of possible local optima and/or to refine its accuracy, resulting in substantially improved quality of global solutions. The APSO algorithm is tested on 6 unimodal and multimodal functions, and the experimental results demonstrate that the APSO generally outperforms the compared PSOs, in terms of solution accuracy, convergence speed and algorithm reliability.
This work was supported by NSF of China Project No.60573066 and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, P.R. China.
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References
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948 (1995)
Li, X.D., Engelbrecht, A.P.: Particle Swarm Optimization: an Introduction and Its Recent Developments. In: Proceedings of the 2007 Genetic Evolutionary Computation Conference, pp. 3391–3414 (2007)
Shi, Y., Eberhart, R.C.: A Modified Particle Swarm Optimizer. In: Proceedings of the IEEE World Congress on Computation Intelligence, pp. 69–73 (1998)
Ratnaweera, A., Halgamuge, S., Watson, H.: Self-organizing Hierarchical Particle Swarm Optimizer with Time-varying Acceleration Coefficients. J. IEEE Trans. Evol. Comput. 8, 240–255 (2004)
Angeline, P.J.: Using Selection to Improve Particle Swarm Optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, Anchorage, AK, pp. 84–89 (1998)
Brits, R., Engelbrecht, A.P., van den Bergh, F.: A Niching Particle Swarm Optimizer. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolutionary Learning, pp. 692–696 (2002)
Parrott, D., Li, X.D.: Locating and Tracking Multiple Dynamic Optima by a Particle Swarm Model Using Speciation. J. IEEE Trans. Evol. Comput. 10, 440–458 (2006)
Yao, X., Liu, Y., Lin, G.M.: Evolutionary Programming Made Faster. J. IEEE Trans. Evol. Comput. 3, 82–102 (1999)
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Zhan, Zh., Zhang, J. (2008). Adaptive Particle Swarm Optimization. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2008. Lecture Notes in Computer Science, vol 5217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87527-7_21
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DOI: https://doi.org/10.1007/978-3-540-87527-7_21
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