Abstract
In this paper, a new multi-swarm method is proposed for multi-objective particle swarm optimization. To enhance the Pareto front searching ability of PSO, the particles are divided into many swarms. Several swarms are dynamically searching the objective space around some points of the Pareto front set. The rest of particles are searching the space keeping away from the Pareto front to improve the global search ability. Simulation results and comparisons with existing Multi-objective Particle Swarm Optimization methods demonstrate that the proposed method effectively enhances the search efficiency and improves the search quality.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference Neural Networks, Perth, Australia, pp. 1942–1948. IEEE Press, New York (1995)
Ho, S.L., Yang, S., Ni, G., Lo, E.W.C., Wong, H.C.: A Particle Swarm Optimization-based Method for Multiobjective Design Optimizations. IEEE Trans. on Magn. 41, 1756–1759 (2005)
Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing Hierarchical Particle Swarm Optimizer with Time-varying Acceleration Coefficients. IEEE Trans. on Evolu. Comp. 8, 240–255 (2004)
Tan, K.C., Khor, E.F., Lee, T.H.: Evolutionary Multi-objective Optimization: Algorithms and Applications. Springer, New York (2005)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Trans. on Evolu. Comp. 6, 182–197 (2002)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Computation Engineering Networks Lab (TIK), Swiss Fed. Inst. Technol (ETH), Zurich, Switzerland, Tech. Rep. 103 (2001)
Coello, C., Pulido, C.A., Lechuga, M.S.: Handling Multiple Objectives With Particle Swarm Optimization. IEEE Trans. on Evolu. Comp. 8, 256–279 (2004)
Yen, G.G., Daneshyari, M.: Diversity-based Information Exchange Among Multiple Swarms in Particle Swarm Optimization. Int. J. Compu. Intel. Appl. 7, 57–75 (2008)
Leong, W.F., Yen, G.G.: PSO-based Multi-objective Optimization with Dynamic Population Size and Adaptive Local Archives. IEEE Tran. Syst., Man, Cyb. B, Cyb. 38, 1270–1293 (2008)
Cooren, Y., Clerc, M., Siarry, P.: MO-TRIBES, Adaptive Multiobjective Particle Swarm Optimization Algorithm, Compu. Opt. and Appl. 30(2), 60–80 (2010)
Cooren, Y., Clerc, M., Siarry, P.: Performance Evaluation of TRIBES, Adaptive Particle Swarm optimization algorithm. Swarm Intel. 3, 149–178 (2009)
Khor, E.F., Tan, K.C., Lee, T.H., Goh, C.K.: A Study on Distribution Preservation Mechanism in Evolutionary Multi-Objective Optimization. Artificial Intel. Rev. 23, 31–56 (2005)
Fonseca, C.M., Fleming, P.J.: Genetic Algorithm for Multi-objective Optimization, Formulation, Discussion and Generalization. In: Genetic Algorithms: Proceedings of the Fifth International Conference, pp. 416–423 (1993)
Khor, E.F., Tan, K.C., Lee, T.H., Goh, C.K.: A Study on Distribution Preservation Mechanism in Evolutionary Multi-Objective Optimization. Artificial Intelligence Review 23, 31–56 (2005)
Jeong, S., Hasegawa, S., Shimoyama, K., Obayashi, S.: Development and Investigation of Efficient GA/PSO-Hybrid Algorithm Applicable to Real-World Design Optimization. IEEE Compu. Intel. Mag. 30(2), 36–44 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, Y., van Wyk, B.J., Wang, Z. (2012). A New Multi-swarm Multi-objective Particle Swarm Optimization Based on Pareto Front Set. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-25944-9_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25943-2
Online ISBN: 978-3-642-25944-9
eBook Packages: Computer ScienceComputer Science (R0)