Abstract
Methods to deal with Multimodal Optimization Problems (MMOP) can be classified in three main approaches, regarding the number and the type of desired solutions. In general, methods can be applied to find: (1) only one global solution; (2) all global solutions; and (3) all local solutions of a given MMOP. The simultaneous capture of several solutions of MMOPs without parameter adjustment is still an open question in optimization problems. In this article, we discuss a density segregation mechanism for Fish School Search to enable simultaneous capture of multiple optimal solutions of MMOPs with one single parameter. The new proposal is based on vanilla version of Fish School Search (FSS) algorithm, which is inspired on actual fish school behavior. The performance of the new algorithm is evaluated and compared to the performance of other methods such as NichePSO and Glowworm Swarm Optimization (GSO) for seven well-known benchmark functions of two dimensions. According to the obtained results, presented in this article, the new approach outperforms the algorithms NichePSO and GSO for all benchmark functions.
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References
Koper, K., Wysession, M., Wiens, D.: Multimodal function optimization with a niching genetic algorithm: A seismological example. Bulletin of the Seismological Society of America 89(4), 978–988 (1999)
Dilettoso, E., Salerno, N.: A self-adaptive niching genetic algorithm for multimodal optimization of electromagnetic devices. IEEE Transactions on Magnetics 42(4), 1203–1206 (2006)
El Imrani, A., Zine El Abidine, H., Limouri, M., Essaid, A.: Multimodal optimization of thermal histories. Comptes Rendus de l’Academie de Sciences - Serie IIa: Sciences de la Terre et des Planetes 329(8), 573–577 (1999)
Luh, G.-C., Chueh, C.-H.: Job shop scheduling optimization using multi-modal immune algorithm. In: Okuno, H.G., Ali, M. (eds.) IEA/AIE 2007. LNCS (LNAI), vol. 4570, pp. 1127–1137. Springer, Heidelberg (2007)
Naraharisetti, P., Karimi, I., Srinivasan, R.: Supply chain redesigns - multimodal optimization using a hybrid evolutionary algorithm. Industrial and Engineering Chemistry Research 48(24), 11094–11107 (2009)
Krishnanand, K., Ghose, D.: Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intelligence 3(2), 87–124 (2009)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. Micro Machine and Human Science, 39–43 (1995)
Shi, Y., Eberhart, R.C.: An empirical study of particle swarm optimization. In: IEEE Congress on Evolutionary Computation, pp. 1945–1960 (1999)
Parsopoulos, K., Vrahatis, M.N.: Modification of the particle swarm optimizer for locating all the global minima. In: Karny (ed.) Artificial Neural Networks and Genetic Algorithms, pp. 324–327 (2001)
Brits, R., Engelbrecht, A.P., van den Bergh, F.: A niching particle swarm optimizer. In: Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning, pp. 692–696 (2002)
Parsopoulos, K., Vrahatis, M.N.: On the computation of all global minimizers through particle swarm optimization. IEEE Transactions on Evolutionary Computation 8(3), 211–224 (2004)
Brits, R., Engelbrecht, A., van den Bergh, F.: Locating multiple optima using particle swarm optimization. Applied Mathematics and Computation 189(2), 1859–1883 (2007)
Ozcan, E., Yilmaz, M.: Particle swarms for multimodal optimization. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. LNCS, vol. 4431, pp. 366–375. Springer, Heidelberg (2007)
Engelbrecht, A., Van Loggerenberg, L.: Enhancing the nichepso. In: IEEE Congress on Evolutionary Computation, CEC, pp. 2297–2302 (2007)
Bastos-Filho, C., de Lima Neto, F., Lins, A., Nascimento, A., Lima, M.: Fish school search. In: Chiong, R. (ed.) Nature-Inspired Algorithms for Optimisation. SCI, vol. 193, pp. 261–277. Springer, Heidelberg (2009)
Martinetz, T.M., Schulten, K.J.: Topology representing networks. Neural Networks 7(3), 507–522 (1994)
Griewank, A.: Generalized descent for global optimization. Journal of Optimization Theory and Applications 34, 11–39 (1981)
Thiemard, E.: Economic generation of low-discrepancy sequences with a b-ary gray code. Technical report, Departement de Mathematiques, Ecole Polytechnique Federale de Lausanne, CH-1015 Lausanne, Switzerland (1998)
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Madeiro, S.S., de Lima-Neto, F.B., Bastos-Filho, C.J.A., do Nascimento Figueiredo, E.M. (2011). Density as the Segregation Mechanism in Fish School Search for Multimodal Optimization Problems. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_69
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DOI: https://doi.org/10.1007/978-3-642-21524-7_69
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