Abstract
How to find a sufficient number of uniformly distributed and representative Pareto optimal solutions is very important for Multiobjective Optimization (MO) problems. An Intelligent Particle Swarm Optimization (IPSO) for MO problems is proposed based on AER (Agent-Environment-Rules) model, in which competition and clonal selection operator are designed to provide an appropriate selection pressure to propel the swarm population towards the Pareto-optimal Front. An improved measure for uniformity is carried out to the approximation of the Pareto-optimal set. Simulations and comparison with NSGA-II and MOPSO indicate that IPSO is highly competitive.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc. 6th Int. Symposium on Micro machine and Human Science, Nagoya, pp. 39–43 (1995)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE Int. Conf. on Neural Networks, Perth, pp. 1942–1948 (1995)
Coello, C.C., Lechunga, M.S.: A proposal for Multiple Objective Particle Swarm Optimization. In: Proceedings of the IEEE World Congress on Computational Intelligence, Hawaii, May 12-17. IEEE Press, Los Alamitos (2002)
Coello, C.C., Pulido, G.T., Lechuga, M.S.: Handling Multiple Objectives with Particle Swarm Optimization. IEEE Trans. on Evolutionary Computation 8(3), 256–279 (2004)
Hu, X., Eberhart, R.C., Shi, Y.: Particle Swarm with extended memory for Multi-Objective Optimization. In: Proc. 2003 IEEE Swarm Intelligence Symp. Indianapolis, IN, April 2003, pp. 193–197 (2003)
Moore, J., Chapman, R.: Application of Particle Swarm to Multi-Objective Optimization. Dept. Comput. Sci. Software Eng. Auburn Univ. (1999)
Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization method in multi-objective Problems. In: Proceedings of the 2002 ACM Symposium on Applied Computing (SAC 2002), pp. 603–607 (2002)
Ray, T., Liew, K.M.: A Swarm Metaphor for Multiobjective design Optimization. Eng. Opt. 34(2), 141–153 (2002)
Deb, K., Pratap, A., Agrawal, S., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA- II. IEEE Trans. on Evolutionary Computation 6(2), 182–197 (2002)
Zitzler, Thiele: Multi-objective Evolutionary Algorithm: A Comparative Case Study and the Strength Pareto Approach. IEEE Trans. on EC 3(4), 257–271 (1999)
Liu, J.M., Jing, H., Tang, Y.Y.: Multi-Agent oritened constraint satisfaction. Artificial Intelligence 1(136), 101–144 (2002)
Lu, D., Ma, B.: Modern Immunology. Shanghai Scientific and Technological Education Publishing House, Shanghai (1998) (in Chinese)
Schott, J.R.: Fault tolerant design using single and multicriteria genetic algorithm optimization. M.S. thesis, Dept. Aeronautics and Astronautics, Massachusetts Inst. Technol., Cambridge, MA (May 1995)
Veldhuizen, D.A.V., Lamont, G.B.: Multiobjective Evolutionary Algorithm Research: history and analysis. Dept. Elec. Comput. Eng., Graduate School of Eng., Air Force Inst.Technol., Wright-Patterson AFB, OH. Tech. Rep. TR-98-03 (1998)
Van Veldhuizen, D.A.: Multiobjective evolutionary algorithms: Classifications, analyzes, and new innovations. Ph.D. dissertation, Dept. Elec. Compt. Eng., Graduate School of Eng., Air Force Inst.Technol., Wright-Patterson AFB, OH (May 1999)
Zitzler, E.: Evolutionary Algorithms for Multi-objective Optimization: Methods and Applications. Ph.D. Thesis, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (November 1999)
Zitzler, Thiele: Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Trans. on Evolutionary Computation 7(2), 117–132
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meng, Hy., Zhang, Xh., Liu, Sy. (2005). Intelligent Multiobjective Particle Swarm Optimization Based on AER Model. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_18
Download citation
DOI: https://doi.org/10.1007/11595014_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30737-2
Online ISBN: 978-3-540-31646-6
eBook Packages: Computer ScienceComputer Science (R0)