Abstract
Particle swarm optimization (PSO) is a metaheuristic where a swarm of particles moves within a search space in order to find an optimal solution. PSO has been applied to continuous and combinatorial optimization problems in various application areas. As is typical for metaheuristics, it is also not easy for PSO for algorithm designers to understand in detail how and why changes in the design of a PSO algorithm influence its optimization behavior. It is shown in this chapter that a suitable visualization of the optimization process can be very helpful for understanding the optimization behavior of PSO algorithms. In particular, it is explained how the visualization tool dPSO-Vis can be used to analyze the optimization behavior of PSO algorithms. The two example PSO algorithms that are used are the SetPSO and the HelixPSO. Both algorithms can be used for solving the RNA secondary structure prediction problem.
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.C., Kennedy, J.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)
Engelbrecht, A.P.: Computational Intelligence: An Introduction. John Wiley and Sons, Chichester (2002)
Flamm, C., Fontana, W., Hofacker, I.L., Schuster, P.: RNA folding at elementary step resolution. RNA 6, 325–338 (2000)
Flamm, C., Hofacker, I.L., Stadler, P.F., Wolfinger, M.T.: Barrier trees of degenerate landscapes. Z. Phys. Chem. 216, 1–19 (2002)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman, New York (1979)
Geis, M.: Secondary Structure Prediction of Large RNAs. Ph.D. thesis, Universität Leipzig (2008)
Geis, M., Middendorf, M.: A particle swarm optimizer for finding minimum free energy RNA secondary structures. In: Proc. IEEE Swarm Intelligence Symposium, pp. 1–8 (2007)
Geis, M., Middendorf, M.: Particle swarm optimization for finding RNA secondary structures. International Journal of Intelligent Computing and Cybernetics 4, 160–186 (2011)
Heine, C., Scheuermann, G., Flamm, C., Hofacker, I.L., Stadler, P.F.: Visualization of barrier tree sequences. IEEE Transactions on Visualization and Computer Graphics 12, 781–788 (2006)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the International Conference on Neural Networks, pp. 1942–1948 (1995)
Neethling, C.M.: Using SetPSO to determine RNA secondary structure. Ph.D. thesis, University of Pretoria (2008)
Neethling, M., Engelbrecht, A.: Determining RNA secondary structure using set-based particle swarm optimization. In: Proc. IEEE Congress on Evolutionary Computation, CEC 2006, pp. 1670 –1677 (2006)
Oesterling, P., Heine, C., Jänicke, H., Scheuermann, G., Heyer, G.: Visualization of high-dimensional point clouds using their density distribution’s topology. IEEE Transactions on Visualization and Computer Graphics 17, 1547–1559 (2011)
Secrest, B.R., Lamont, G.B.: Visualizing particle swarm optimization - Gaussian particle swarm optimization. In: Proc. IEEE Swarm Intelligence Symposium, pp. 198–204 (2003)
Volke, S., Middendorf, M., Hlawitschka, M., Kasten, J., Zeckzer, D., Scheuermann, G.: dPSO-Vis: Topology-based visualization of discrete particle swarm optimization. Computer Graphics Forum 32, 351–360 (2013)
Weber, G., Bremer, P.T., Pascucci, V.: Topological landscapes: A terrain metaphor for scientific data. IEEE Transactions on Visualization and Computer Graphics 13, 1416–1423 (2007)
Xin, B., Chen, J., Pan, F.: Problem difficulty analysis for particle swarm optimization: deception and modality. In: Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 623–630 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Volke, S., Bin, S., Zeckzer, D., Middendorf, M., Scheuermann, G. (2014). Visual Analysis of Discrete Particle Swarm Optimization Using Fitness Landscapes. In: Richter, H., Engelbrecht, A. (eds) Recent Advances in the Theory and Application of Fitness Landscapes. Emergence, Complexity and Computation, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41888-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-41888-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41887-7
Online ISBN: 978-3-642-41888-4
eBook Packages: EngineeringEngineering (R0)