Abstract
A novel multiobjective constriction particle swarm optimization (MOCPSO) is presented. MOCPSO not only uses mutation operator to avoid earlier convergence and uses adaptive weight to raise the search capacity, but also uses a new crowding operator to improve the distribution of nondominated solutions along the Pareto front, and uses the uniform design to obtain the optimal parameter combination. The sound evaluation criteria for multiobjective optimization algorithm are given, and some typical test functions are introduced. Experimental results show that MOCPSO has faster convergent speed and better search capacity than other multiobjective particle swarm optimization algorithms, especially when there are more than two objectives.
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.
References
Coello, C.A., Pulido, G.T., Lechuga, M.S.: Handling Multiple Objectives with Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation 3, 256–279 (2004)
Huang, V.L., Suganthan, P.N., Liang, J.J.: Comprehensive Learning Particle Swarm Optimizer for Solving Multiobjective Optimization Problems. Int. J. Intell. Syst. 2, 209–226 (2006)
Reyes-Sierra, M., Coello, C.A.: Multi-objective Particle Swarm Optimizers: A Survey of the State-of-The-Art. Int. J. Comput. Intell. Research 3, 287–308 (2006)
Deb, K., Pratap, A., Agarwal, S., et al.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 2, 182–197 (2002)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks (ICNN95), Perth, pp. 1942–1948. IEEE Computer Society Press, Los Alamitos (1995)
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Mateo (2001)
Kennedy, J., Mendes, R.: Neighborhood Topologies in Fully Informed and Best-of-Neighborhood Particle Swarms. IEEE Trans. Syst. Man Cybern. Pt. C: Appl. Rev. 4, 515–519 (2006)
Shi, Y., Eberhart, R.C.: A Modified Particle Swarm Optimizer. In: Proceedings of IEEE International Conference of Evolutionary Computation, Anchorage, pp. 69–73 (1998)
Leung, Y.W., Wang, Y.P.: Multiobjective Programming Using Uniform Design and Genetic Algorithm. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews 3, 293–304 (2000)
Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evol. Comput. 2, 173–195 (2000)
Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective Evolutionary Algorithm Research: A History and Analysis. Air Force Inst. Technol. Wright-Patterson AFB, OH, Tech. Rep. TR-98-03 (1998)
Czyzak, P., Jaszkiewicz, A.: Pareto-Simulated Annealing – A Metaheuristic Technique for Multi-Objective Combinatorial Optimization. Journal of Multi-Criteria Decision Analysis 1, 34–47 (1998)
Schott, J.R.: Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. M.S. thesis, Massachusetts Inst. Technol. Cambridge, MA (1995)
Van Veldhuizen, D.A.: Multiobjective Evolutionary Algorithms: Classifications, Analyzes, and New Innovations. Ph.D. Dissertation, Graduate School of Eng. Air Force Inst. Technol. Wright-Patterson AFB, OH (1999)
Kita, H., Yabumoto, Y., Mori, N., Nishikawa, Y.: Multi-objective Optimization by Means of the Thermodynamical Genetic Algorithm. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature - PPSN IV. LNCS, vol. 1141, pp. 504–512. Springer, Heidelberg (1996)
Kursawe, F.: A Variant of Evolution Strategies for Vector Optimization. In: Schwefel, H.-P., Männer, R. (eds.) Parallel Problem Solving from Nature. LNCS, vol. 496, pp. 193–197. Springer, Heidelberg (1991)
Deb, K.: Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evol. Comput. 3, 205–230 (1999)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Test Problems for Evolutionary Multi-Objective Optimization. Technical Report 112, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Niu, Y., Shen, L. (2007). Multiobjective Constriction Particle Swarm Optimization and Its Performance Evaluation. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_117
Download citation
DOI: https://doi.org/10.1007/978-3-540-74205-0_117
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74201-2
Online ISBN: 978-3-540-74205-0
eBook Packages: Computer ScienceComputer Science (R0)