Abstract
The size, scope and variety of the experimental analyses of metaheuristics has increased in recent years, aiming to develop new procedures and techniques to improve our understanding of optimization algorithms and problems. In this paper, we compare particle swarm optimization and differential evolution on a set of real-world clustering problems. Generally, experimental comparisons focus on presenting a statistical summary of algorithm performance, however this hides valuable information about the algorithm behaviour on the problems in question. Instead, we take an exploratory approach, focussing on extracting deeper insights and understanding from the experimental results data. We make progress on understanding the fitness landscapes of the set of clustering problems, as well as analysing current and previous experimental results for algorithms applied to these problems. Consequently, the paper makes two contributions: (a) Advancing our understanding of what factors make this set of problem instances easy or hard for given algorithms; (b) Demonstrating the need to be careful in experimental evaluations and that better insights can be obtained with exploratory analysis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alam, S., Dobbie, G., Koh, Y.S., Riddle, P., Rehman, S.U.: Research on particle swarm optimization based clustering: a systematic review of literature and techniques. Swarm Evol. Comput. 17, 1–13 (2014)
Alpaydin, E.: Introduction to machine learning. MIT press, Cambridge (2014)
Bagirov, A.M.: Modified global k-means algorithm for minimum sum-of-squares clustering problems. Pattern Recogn. 41(10), 3192–3199 (2008)
Bartz-Beielstein, T.: Experimental Research in Evolutionary Computation: The New Experimentalism. Springer-Verlag New York Inc., New York (2006)
Berthier, V.: Progressive differential evolution on clustering real world problems. In: Bonnevay, S., Legrand, P., Monmarché, N., Lutton, E., Schoenauer, M. (eds.) EA 2015. LNCS, vol. 9554, pp. 71–82. Springer, Heidelberg (2016). doi:10.1007/978-3-319-31471-6_6
Bubeck, S., Meila, M., von Luxburg, U.: How the initialization affects the stability of the k-means algorithm. arXiv preprint arxiv:0907.5494 (2009)
Chang, D.X., Zhang, X.D., Zheng, C.W.: A genetic algorithm with gene rearrangement for k-means clustering. Pattern Recogn. 42(7), 1210–1222 (2009)
Cohen, S.C., de Castro, L.N.: Data clustering with particle swarms. In: 2006 IEEE International Conference on Evolutionary Computation, pp. 1792–1798. IEEE (2006)
Du Merle, O., Hansen, P., Jaumard, B., Mladenovic, N.: An interior point algorithm for minimum sum-of-squares clustering. SIAM J. Sci. Comput. 21(4), 1485–1505 (1999)
Esmin, A.A.A., Pereira, D.L., De Araujo, F.: Study of different approach to clustering data by using the particle swarm optimization algorithm. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 1817–1822. IEEE (2008)
Gallagher, M., Yuan, B.: A general-purpose, tunable landscape generator. IEEE Trans. Evol. Comput. 10(5), 590–603 (2006)
Gallagher, M.: Towards improved benchmarking of black-box optimization algorithms using clustering problems. Soft Comput. 20, 1–15 (2016)
Hansen, N., Finck, S., Ros, R., Auger, A.: Real-parameter black-box optimization benchmarking 2009: noiseless functions definitions. Research report RR-6829, INRIA (2009)
Hansen, P., Mladenović, N.: J-means: a new local search heuristic for minimum sum of squares clustering. Pattern Recogn. 34(2), 405–413 (2001)
Karthi, R., Arumugam, S., Rameshkumar, K.: Comparative evaluation of particle swarm optimization algorithms for data clustering using real world data sets. IJCSNS Int. J. Comput. Sci. Netw. Secur. 8(1), 203–212 (2008)
Liang, J., Qu, B., Suganthan, P.: Problem definitions and evaluation criteria for the cec 2014 special session and competition on single objective real-parameter numerical optimization. Technical report 201311, Nanyang Technological University, Singapore (2013)
Locatelli, M.: A note on the Griewank test function. J. Global Optim. 25(2), 169–174 (2003)
McGeoch, C.: Experimental algorithmics. Commun. ACM 50(11), 27–31 (2007)
Mersmann, O., Preuss, M., Trautmann, H.: Benchmarking evolutionary algorithms: towards exploratory landscape analysis. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN 2010. LNCS, vol. 6238, pp. 73–82. Springer, Berlin (2010). doi:10.1007/978-3-642-15844-5_8
Van der Merwe, D., Engelbrecht, A.P.: Data clustering using particle swarm optimization. In: The 2003 Congress on Evolutionary Computation, CEC 2003, vol. 1, pp. 215–220. IEEE (2003)
Munoz, M.A., Kirley, M., Halgamuge, S.K.: Exploratory landscape analysis of continuous space optimization problems using information content. IEEE Trans. Evol. Comput. 19(1), 74–87 (2015)
Paterlini, S., Krink, T.: High performance clustering with differential evolution. In: Congress on Evolutionary Computation, CEC 2004, vol. 2. IEEE (2004)
Rana, S., Jasola, S., Kumar, R.: A review on particle swarm optimization algorithms and their applications to data clustering. Artif. Intell. Rev. 35(3), 211–222 (2011)
Rardin, R.L., Uzsoy, R.: Experimental evaluation of heuristic optimization algorithms: a tutorial. J. Heuristics 7, 261–304 (2001)
Rönkkönen, J., Li, X., Kyrki, V., Lampinen, J.: A framework for generating tunable test functions for multimodal optimization. Soft Comput. 15(9), 1689–1706 (2011)
Xu, R., Xu, J., Wunsch, D.C.: Clustering with differential evolution particle swarm optimization. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Saleem, S., Gallagher, M. (2017). Exploratory Analysis of Clustering Problems Using a Comparison of Particle Swarm Optimization and Differential Evolution. In: Wagner, M., Li, X., Hendtlass, T. (eds) Artificial Life and Computational Intelligence. ACALCI 2017. Lecture Notes in Computer Science(), vol 10142. Springer, Cham. https://doi.org/10.1007/978-3-319-51691-2_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-51691-2_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-51690-5
Online ISBN: 978-3-319-51691-2
eBook Packages: Computer ScienceComputer Science (R0)