Abstract
Particle Swarm Optimization (PSO) offers efficient simultaneous global and local searches but is challenged with the problem of slow local convergence. To address this issue, a hybrid comprehensive learning PSO algorithm with adaptive starting local search (ALS-HCLPSO) is proposed. Determining when to start local search is the main of ALS-HCLPSO. A quasi-entropy index is innovatively utilized as the criterion of population diversity to depict an aggregation degree of particles and to ascertain whether the global optimum basin has been explored. This adaptive strategy ensures the proper starting of local search. The test results on eight multimodal benchmark functions demonstrate the performance superiority of ALS-HCLPSO. And comparison results on six advanced PSO variants further test the validity and superiority of ALS-HCLPSO algorithm.
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References
Kenndy, J., Eberhart, R.C.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)
Liang, X., Li, W., Zhang, Y., et al.: An adaptive particle swarm optimization method based on clustering. Soft. Comput. 19(2), 431–448 (2015)
Liang, J.J., Qin, A.K., Suganthan, P.N., et al.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)
Nasir, M., Das, S., Maity, D., et al.: A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization. Inform. Sci. 209, 16–36 (2012)
Lynn, N., Suganthan, P.N.: Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol. Comput. 24, 11–24 (2015)
Luenberger, D.G., Ye, Y.: Linear and Nonlinear Programming, 4th edn. Springer, New York (2015)
Pablo, M.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Technique report, Caltech Concurrent Computation Program, USA (1989)
Zhao, S.Z., Liang, J.J., Suganthan, P.N., et al.: Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization. In: 2008 IEEE Congress on Evolutionary Computation, pp. 3845–3852 (2008)
Han, F., Liu, Q.: An improved hybrid PSO based on ARPSO and the Quasi-Newton Method. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) ICSI 2015. LNCS, vol. 9140, pp. 460–467. Springer, Cham (2015). doi:10.1007/978-3-319-20466-6_48
Parsopoulos, K.E., Vrahatis, M.N.: Unified particle swarm optimization in dynamic environments. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, George D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 590–599. Springer, Heidelberg (2005). doi:10.1007/978-3-540-32003-6_62
Peram, T., Veeramachaneni, K., Mohan, C.K.: Fitness-distance-ratio based particle swarm optimization. In: Proceedings of the IEEE Swarm Intelligence Symposium, pp. 174–181 (2003)
Tanweer, M.R., Suresh, S., Sundararajan, N.: Self regulating particle swarm optimization algorithm. Inform. Sci. 294, 182–202 (2015)
Acknowledgements
This work is supported by National Natural Science Foundation of China (Grants No. 61571336, No. 61603280 and No. 71672137).
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Cao, Y., Li, W., Chaovalitwongse, W.A. (2017). Hybrid Comprehensive Learning Particle Swarm Optimizer with Adaptive Starting Local Search. 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_16
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DOI: https://doi.org/10.1007/978-3-319-61824-1_16
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