Abstract
The concept of optimization is present in several natural processes such as the evolution of species, the behavior of social groups and the ecological relationships of different animal populations. This work uses the concepts of habitats, ecological relationships and ecological successions to build a hybrid cooperative search algorithm, named ECO. The Artificial Bee Colony (ABC) and the Particle Swarm Optimization (PSO) algorithms were used in the experiments where benchmark mathematical functions were optimized. Results were compared with ABC and PSO running alone, and with both algorithms in a well known island model with ring topology, all running without the ecology concepts previously mentioned. The ECO algorithm performed better than the other approaches, especially as the dimensionality of the functions increase, possibly thanks to the ecological interactions (intra and inter-habitats) that enabled the co-evolution of populations. Results suggest that the ECO algorithm can be an interesting alternative for numerical optimization.
This is a preview of subscription content, log in via an institution to check access.
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
de Castro, L.N.: Fundamentals of natural computing: an overview. Physics of Life Reviews 4(1), 1–36 (2007)
Engelbrecht, A.P.: Computational Intelligence: An Introduction, 2nd edn. Wiley, Chichester (2007)
Parpinelli, R.S., Lopes, H.S.: New inspirations in swarm intelligence: a survey. International Journal of Bio-Inspired Computation 3(1), 1–16 (2011)
Begon, M., Townsend, C.R., Harper, J.L.: Ecology: from individuals to ecosystems, 4th edn. Blackwell Publishing, Oxford (2006)
May, R.M.C., McLean, A.R.: Theoretical Ecology: Principles and Applications. Oxford University Press, Oxford (2007)
El-Abd, M., Kamel, M.: A Taxonomy of Cooperative Search Algorithms. In: Blesa, M.J., Blum, C., Roli, A., Sampels, M. (eds.) HM 2005. LNCS, vol. 3636, pp. 32–41. Springer, Heidelberg (2005)
Masegosa, A.D., Pelta, D., del Amo, I.G., Verdegay, J.L.: On the Performance of Homogeneous and Heterogeneous Cooperative Search Strategies. In: Krasnogor, N., Melián-Batista, M.B., Pérez, J.A.M., Moreno-Vega, J.M., Pelta, D.A. (eds.) NICSO 2008. SCI, vol. 236, pp. 287–300. Springer, Heidelberg (2009)
Parpinelli, R.S., Benítez, C.M.V., Lopes, H.S.: Parallel approaches for the artificial bee colony algorithm. In: Panigrani, B.K., Shi, Y., Lim, M. (eds.) Handbook of Swarm Intelligence: Concepts, Principles and Applications. Series: Adaptation, Learning, and Optimization, pp. 329–346. Springer, Berlin (2011)
Benítez, C.M.V., Parpinelli, R.S., Lopes, H.S.: Parallelism, hybridism and coevolution in a multi-level ABC-GA approach for the protein structure prediction problem. In: Concurrency and Computation: Practice and Experience (2011)
Parpinelli, R.S., Lopes, H.S.: An eco-inspired evolutionary algorithm applied to numerical optimization. In: Proceedings of the Third World Congress on Nature and Biologically Inspired Computing, Salamanca, Spain, pp. 473–478 (2011)
Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation 214, 108–132 (2009)
Clerc, M.: Particle Swarm Optimization. ISTE Press (2006)
Blickle, T.: Tournament selection. In: Bäck, T., Fogel, D., Michalewicz, Z. (eds.) Evolutionary Computation, vol. 2, pp. 181–186. Institute of Physics, Bristol (2000)
Digalakis, J.G., Margaritis, K.G.: An experimental study of benchmarking functions for evolutionary algorithms. International Journal of Computer Mathematics 79(4), 403–416 (2002)
Floudas, C.A., Pardalos, P.M.: A Collection of Test Problems for Constrained Global Optimization Algorithms. LNCS, vol. 455. Springer (1990)
Mühlenbein, H., Schomisch, D., Born, J.: The parallel genetic algorithm as function optimizer. Parallel Computing 17(6-7), 619–632 (1991)
Griewank, A.: Generalized descent for global optimization. Journal of Optimization Theory and Applications 34(1), 11–39 (1981)
Cho, H., Olivera, F., Guikema, S.: A derivation of the number of minima of the Griewank function. Applied Mathematics and Computation 204(2), 694–701 (2008)
Rosenbrock, H.: An automatic method for finding the greatest or least value of a function. The Computer Journal 3, 175–184 (1960)
Clerc, M.: Standard PSO 2007, SPSO-07 (2007), http://www.particleswarm.info/Programs.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Parpinelli, R.S., Lopes, H.S. (2012). An Ecology-Based Heterogeneous Approach for Cooperative Search. In: Barros, L.N., Finger, M., Pozo, A.T., Gimenénez-Lugo, G.A., Castilho, M. (eds) Advances in Artificial Intelligence - SBIA 2012. SBIA 2012. Lecture Notes in Computer Science(), vol 7589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34459-6_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-34459-6_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34458-9
Online ISBN: 978-3-642-34459-6
eBook Packages: Computer ScienceComputer Science (R0)