Abstract
Particle swarm optimization is a new globe optimization algorithm based on swarm intelligent search. It is a simple and efficient optimization algorithm. Therefore, this algorithm is widely used in solving the most complex problems. However, particle swarm optimization is easy to fall into local minima, defects and poor precision. As a result, an improved particle swarm optimization algorithm is proposed to deal with multi-modal function optimization in high dimension problems. Elite particles and bad particles are differentiated from the swarm in the initial iteration steps, bad particles are replaced with the same number of middle particles generated by mutating bad particles and elite particles. Therefore, the diversity of particle has been increased. In order to avoid the particles falling into the local optimum, the direction of the particles changes in accordance with a certain probability in the latter part of the iteration. The results of the simulation and comparison show that the improved PSO algorithm named EGAPSO is verified to be feasible and effective.
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References
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc. 6th Int. Symp Micromach. Hum. SCI. Nagoya, Japan, pp. 39–43 (1995)
Wang, Y.F., Zhang, Y.F.: A PSO-based Multi-objective Optimization Approach to the Integration of Process Planning and Scheduling. In: 2010 8th IEEE International Conference on Control and Automation, pp. 614–619 (2010)
Hu, X., Eberhart, R.: Multi-objective optimization using dynamic neighborhood particle swarm optimization. In: Congress on evolutionary computation (CEC), vol. 2, pp. 1677–1681. IEEE Service Center, Piscataway (2002)
Sun, Y., Zhang, W.: Design of Neural Network Gain Scheduling Flight Control Law Using a Modified PSO Algorithm Based on Immune Clone Principle. In: 2009 Second International Conference on Intelligent Computation Technology and Automation, pp. 259–263 (2009)
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. Humans 38(2), 288–298 (2008)
Li, S., Tan, M., Kwok, J.T.-Y.: A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics 41(4) (August 2011)
Coelho, L.S., Krohling, R.A.: Predictive controller tuning using modified particle swarm optimization based on Cauchy and Gaussian distributions. In: Proceedings of the VI Brazilian Conference on Neural Networks, Sao Paulo, Brazil (June 2003) (in Portuguese)
Li, M., Lin, D., Kou, J.: A hybrid niching PSO enhanced with recombination-replacement crowding strategy for multimodal function optimization. Applied Soft Computing 12, 975–987 (2012)
Liang, Y., Leung, K.-S.: Genetic Algorithm with adaptive elitist-population strategies for multimodal function optimization. Applied Soft Computing 11, 2017–2034 (2011)
Zhang, W., Liu, Y.: Adaptive particle swarm optimization for reactive power and voltage control in power systems. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 449–452. Springer, Heidelberg (2005)
Su, C.-T., Wong, J.-T.: Designing MIMO controller by neuro-traveling particle swarm optimizer approach. Expert System with Applications 32, 848–855 (2007)
Yi, W., Yao, M., Jiang, Z.: Fuzzy particle swarm optimization clustering and its application to image clustering. In: Zhuang, Y.-T., Yang, S.-Q., Rui, Y., He, Q. (eds.) PCM 2006. LNCS, vol. 4261, pp. 459–467. Springer, Heidelberg (2006)
Jiao, W., Liu, G., Liu, D.: Elite Particle Swarm Optimization with Mutation. In: 7th Intl. Conf. on Sys. Simulation and Scientific Computing, pp. 800–803 (2008)
Li, X.: Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology. IEEE Transactions on Evolutionary Computation, 150–169 (February 2010)
Norouzzadeh, M.S.: Plowing PSO: A Novel Approach to Effectively Initializing Particle Swarm Optimization. In: 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), pp. 705–708 (2010)
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Yu, H., Li, X. (2013). High Dimensional Problem Based on Elite-Grouped Adaptive Particle Swarm Optimization. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_15
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DOI: https://doi.org/10.1007/978-3-642-39482-9_15
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