Abstract
This paper presents a novel swarm intelligence algorithm named as Butterfly Mating Optimization (BMO) which is based on the mating phenomena occurring in butterflies. The BMO algorithm is developed with novel concept of dynamic local mate selection process which plays a major role in capturing multiple peaks for multimodal search spaces. This BMO algorithm was tested on 3-peaks function and various convergence plots were drawn from it. Also, BMO was tested on other benchmark functions to check and discuss thoroughly its capability in terms of capturing the local peaks. Various comparisons were made between BMO and GSO, a recent swarm algorithm for multimodal optimization problems. BMO was also tested on a function with varying dimensionality at higher level. Finally based on various assumptions through simulations, possible future work is discussed.
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
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press (1999)
Goldberg, D.E., Richardson, J.: Genetic algorithms with sharing for multi-modal function optimization. In: Grefenstette, (ed.) Genetic Algorithms and their Applications, ICCGA 1987, pp. 41–49 (1987)
Lung, R.I., Dumitrescu, D.: Roaming optimization: A new evolutionary technique for multimodal optimization. Studia Univ. Babes Bolyai, Informatica XLIX(1), 99–109 (2004)
Muller, S.D., Marchetto, J., Airaghi, S., Koumoutsakos, P.: Optimization based on bacterial chemotaxis. IEEE Transactions on Evolutionary Computation 6(6), 16–29 (2002)
Deneubourg, J.L., Aron, S., Goss, S., Pasteels, J.M.: The self-organizing exploratory pattern of the Argentine ant. Journal of Insect Behaviour 3(2), 159–168 (1990)
Bilchev, G., Parmee, I.C.: The ant colony metaphor for searching continuous design spaces. In: Fogarty, T.C. (ed.) AISB-WS 1995. LNCS, vol. 993, pp. 25–39. Springer, Heidelberg (1995)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. A Bradford Book. The MIT Press, Cambridge (2004)
Krishnanand, K.N., Ghose, D.: Glowworm swarm optimisation: a new method for optimising multi-modal functions. International Journal of Computational Intelligence Studies 1(1), 93–119 (2009)
Clerc, M.: Particle Swarm Optimization. Hermes Science Publications (April 2006)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the Fourth IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Service Center, Perth (1995)
Parsopoulos, K.E., Plagianakos, V.P., Magoulas, G.D., Vrahatis, M.N.: Stretching technique for obtaining global minimizers through particle swarm optimization. In: Proceedings of Particle Swarm Optimization Workshop, pp. 22–29 (2001)
Rutowski, R.L.: Sexual Selection and the Evolution of Butterfly Mating behaviour. Journal of Research on Lepidoptera, 125–142 (1984)
Andersson, J., Borg-Karlson, A.K., Vongvanich, N.: Wiklund, C.: Male sex pheromone release and female mate choice in a butterfly. Journal of Experimental Biology, 964–970 (2007)
Robertson, K.A., Monteiro, A.: Female Bicyclus Anynana butterflies choose males on the basis of their dorsal UV reflective eyespots. Proceedings of the Biological Sciences/The Royal Society, 1541–1546 (2005)
Sowmya, C., Shaik, A., Jada, C., Vadathya, A.K.: Butterfly communication strategies: a prospect for soft-computing techniques. In: Proceedings of International Joint Conference on Neural Networks (ICJNN), pp. 424–431 (July 2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Jada, C., Vadathya, A.K., Shaik, A., Charugundla, S., Ravula, P.R., Rachavarapu, K.K. (2016). Butterfly Mating Optimization. In: Berretti, S., Thampi, S., Srivastava, P. (eds) Intelligent Systems Technologies and Applications. Advances in Intelligent Systems and Computing, vol 384. Springer, Cham. https://doi.org/10.1007/978-3-319-23036-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-23036-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23035-1
Online ISBN: 978-3-319-23036-8
eBook Packages: EngineeringEngineering (R0)