Abstract
In recent years a number of web-technology supported communities of humans have been developed. Such a web community is able to let emerge a collective intelligence with a higher performance in solving problems than the single members of the community. Thus, collective intelligence systems are explicitly designed to take advantage of these increased capabilities. A well-known collective intelligence system is Wikipedia, the web encyclopedia. It uses a collaborative web community of authors, which improves and completes the content of articles. The quality of a certain number of these articles comes close to some degree to that of a famous printed encyclopedia. Based on such successes of collective intelligence systems, the question arises, whether such a collaborative web community could also be capable of function optimization.
This paper introduces an optimization algorithm called Community Optimization (CO), which optimizes a function by simulating a collaborative web community, which edits or improves an article-base, or, more general, a knowledge-base. The knowledge-base represents the problem to be solved and is realized as a real valued vector. The different vector components (decision variables) represent different topics contained in this knowledge-base. Thus, the dimension of the problem is the number of topics to be improved by the simulated community, whereby the dimension remains static. In order to realize this, CO implements a behavioral model of collaborative human communities derived from the human behavior that can be observed within certain web communities (e.g., Wikipedia or open source communities). The introduced CO method is applied to eight well-known benchmark problems for lower as well as higher dimensions. CO turns out to be the best choice in 9 cases and the Fully Informed Particle Swarm Optimization (FIPS) as well as Differential Evolution (DE) approaches in 4 cases. Concerning the high dimensional problems, CO significantly outperformed FIPS as well as DE in 6 of 8 cases and seems to be a suitable approach for high dimensional problems.
Keywords
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
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence: from natural to artificial systems. /Oxford Press (1999)
Clerc, M., Kennedy, J.: The particle swarmExplosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)
Gardner, M., McNabb, A., Seppi, K.: A speculative approach to parallelization in particle swarm optimization. Swarm Intelligence, 1–40 (2011), doi:10.1007/s11721-011-0066-8; Published online first: December 21, 2011
Giles, J.: Internet encyclopaedias go head to head. Nature 438, 900–901 (2005), doi:10.1038/438900a
Herrera, F., Lozano, M., Molina, D.: Test suite for the special issue of soft computing on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems, http://sci2s.ugr.es/eamhco/updated-functions1-19.pdf (retrieved on: February 8, 2012)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, Perth, Australia. IEEE Service Center, Piscataway (1995)
Kennedy, J.: The particle swarm: social adaptation of knowledge. In: Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, pp. 303–308 (1997), doi:10.1109/ICEC.1997.592326
Kennedy, J.: Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance. In: Proceedings of the 1999 IEEE International Conference on Evolutionary Computation, vol. 3, pp. 1931–1938 (1999)
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers (2001) ISBN: 1-55860-595-9
Kennedy, J., Mendes, R.: Population structure and particle swarm performance. Proceedings of the 2002 Congress on Evolutionary Computation 2, 1671–1676 (2002)
Linux, Linux web site, http://www.linux.org (retrieved on June 3, 2013)
Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: Simpler, maybe better. IEEE Transactions on Evolutionary Computation 8(3), 204–210 (2004)
Monson, C.K., Seppi, K.D.: Exposing origin-seeking bias in PSO. In: Proc. of the 2005 Conference on Genetic and Evolutionary Computation (GECCO 2005), pp. 241–248. ACM, New York (2005), doi:10.1145/1068009.1068045
Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. Proceedings of the 2005 IEEE Congress on Evolutionary Computation 2, 1785–1791 (2005)
Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. Computer Graphics 21, 25–33 (1987)
Shi, Y.H., Eberhart, R.C.: A Modified Particle Swarm Optimizer. In: IEEE International Conference on Evolutionary Computation, Anchorage, Alaska (1998)
Storn, R., Price, K.: Differential Evolution - A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces, Univ. California, Berkeley, ICSI, Technical Report TR-95-012 (March 1995), Downloadable from ftp://ftp.icsi.berkeley.edu/pub/techreports/1995/tr-95-012.pdf
Storn, R.: On the Usage of Differential Evolution for Function Optimization. In: 1996 Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS 1996), Berkeley, pp. 519–523. IEEE, USA (1996)
Storn, R., Price, K.: Differential Evolution - A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization 11(4), 341–359 (1997)
Sutton, A.M., Lunacek, M., Whitley, L.D.: Differential Evolution and Non-separability: Using selective pressure to focus search. In: Proc. of the 2007 Conference on Genetic and Evolutionary Computation (GECCO 2007), pp. 1428–1435. ACM, New York (2007) ISBN:1-59593-697-4
Veenhuis, C.B.: Tree Based Differential Evolution. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 208–219. Springer, Heidelberg (2009)
Veenhuis, C.: Community Optimization: Function Optimization by a Simulated Web Community. In: Proc. of the 12th International Conference on Intelligent Systems Design and Applications (ISDA 2012), Kochi, India, November 27-November 29, pp. 508–514. IEEE (2012); IEEE Catalog Number: CFP12394-CDR, ISBN: 978-1-4673-5118-8
Wikipedia, Wikipedia main page, http://en.wikipedia.org/wiki/Main_Page (retrieved on June 3, 2013)
Wiktionary Edit War, Wiktionary: edit war, http://en.wiktionary.org/wiki/edit_war (retrieved on June 3, 2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Veenhuis, C.B. (2013). Community Optimization. In: Gavrilova, M.L., Tan, C.J.K., Abraham, A. (eds) Transactions on Computational Science XXI. Lecture Notes in Computer Science, vol 8160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45318-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-45318-2_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-45317-5
Online ISBN: 978-3-642-45318-2
eBook Packages: Computer ScienceComputer Science (R0)