Abstract
Optimization problems with many objectives open new issues for multi-objective optimization algorithms and particularly Particle Swarm Optimization. Many of the existing algorithms are able to solve problems of low number of objectives, but as soon as we increase the number of objectives, their performances get even worse than random search methods. This paper gives an overview on Multi-objective Particle Swarm Optimization when having many objectives and parameters. Furthermore, two new variants of MOPSO are proposed which are based on ranking of the non-dominated solutions. The proposed distance based ranking in MOPSO improves the quality of the solutions for even very large objective and parameter spaces. The quality of the new proposed MOPSO methods has been tested and compared to the random search and NSGA-II methods. The tests cover 3 to 20 objectives and 20 to 100 parameters.
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
Alvarez-Benitez, J., Everson, R., Fieldsend, J.: A MOPSO algorithm based exclusively on Pareto dominance concepts. In: Evolutionary Multi-Criterion Optimization, pp. 459–473 (2005)
Bentley, P., Wakefield, J.: Finding acceptable Pareto-optimal solutions using multiobjective genetic algorithms. In: Soft Computing in Engineering Design and Manufacturing. Springer, Heidelberg (1997)
Branke, J., Mostaghim, S.: About selecting the personal best in multi-objective particle swarm optimization. In: Parallel Problem Solving from Nature, pp. 523–532 (2006)
Coello Coello, C., Van Veldhuizen, D., Lamont, G.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, Dordrecht (2002)
Corne, D., Knowles, J.: Techniques for highly multiobjective optimisation: Some nondominated points are better than others. In: Genetic and Evolutionary Computation Conference (2007)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley, Chichester (2001)
Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Parallel Problem Solving from Nature, pp. 849–858 (2000)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: Congress on Evolutionary Computation, pp. 825–830 (2002)
di Pierro, F., Djordjevic, S., Khu, S., Savic, D., Walters, G.: Automatic calibration of urban drainage model using a novel multi-objective GA. In: Urban Drainage Modelling, pp. 41–52 (2004)
Drechsler, D., Drechsler, R., Becker, B.: Multi-objective optimisation based on relation favour. In: Evolutionary Multi-Criterion Optimization, pp. 156–166 (2001)
Engelbrecht, A.: Fundamentals of Computational Swarm Intelligence. John Wiley, Chichester (2006)
Fieldsend, J., Singh, S.: A multi-objective algorithm based upon particle swarm optimisation, an efficient data structure and turbulence. In: The U.K. Workshop on Computational Intelligence, pp. 34–44 (2002)
Kennedy, J., Eberhart, R.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)
Knowles, J., Corne, D.: Quantifying the effects of objective space dimension in evolutionary multiobjective optimization. In: Evolutionary Multi-Criterion Optimization, pp. 757–771 (2007)
Li, X.: A non-dominated sorting particle swarm optimizer for multiobjective optimization. In: Genetic and Evolutionary Computation, pp. 37–48 (2003)
Li, X.: Better spread and convergence: Particle swarm multiobjective optimization using the maximin fitness function. In: Genetic and Evolutionary Computation, pp. 117–128 (2004)
Lovberg, M., Krink, T.: Extending particle swarm optimization with self-organized criticality. In: Conference on Evolutionary Computation, pp. 1588–1593 (2002)
Mostaghim, S., Teich, J.: Strategies for finding good local guides in multi-objective particle swarm optimization. In: IEEE Swarm Intelligence Symposium (2003)
Purshouse, R.: On the Evolutionary Optimisation of Many Objectives. PhD thesis, Department of Automatic Control and Systems Engineering, University of Sheffield, UK (2003)
Purshouse, R., Fleming, P.: Evolutionary multi-objective optimisation: An exploratory analysis. In: Congress on Evolutionary Computation, pp. 2066–2073 (2003)
Reyes-Sierra, M., Coello Coello, C.: Multi-objective particle swarm optimizers: A survey of the state-of-the-art. International Journal of Computational Intelligence Research 2(3), 287–308 (2006)
Xie, X., Zhang, W., Yang, Z.: Adaptive particle swarm optimization on individual level. In: The Sixth International Conference on Signal Processing, pp. 1215–1218 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mostaghim, S., Schmeck, H. (2008). Distance Based Ranking in Many-Objective Particle Swarm Optimization. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_75
Download citation
DOI: https://doi.org/10.1007/978-3-540-87700-4_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87699-1
Online ISBN: 978-3-540-87700-4
eBook Packages: Computer ScienceComputer Science (R0)