Abstract
In this work we introduce two new mechanisms for the Fish School Search algorithm in order to improve the search ability of its original and niching versions. Two modifications in the usual operators are proposed aiming to increase weight parameters reliability and also to include elitist behavior. Five benchmark optimization problems were employed to evaluate the effectiveness of the modifications proposed. We analyze the convergence curves and also the minimum mean fitness obtained by each version. The results show that the proposed mechanisms improved the convergence of the niching version of the Fish School Search algorithm.
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
Similar content being viewed by others
References
Glover, F.W., Kochenberger, G.A.: Handbook of Metaheuristics, vol. 57. Springer, Heidelberg (2006)
Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–72 (1992)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, New York, vol. 1, pp. 39–43 (1995)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 26(1), 29–41 (1996)
Filho, C.J.A.B., De Lima Neto, F.B., Lins, A.J.C.C., Nascimento, A.I.S., Lima, M.P.: A novel search algorithm based on fish school behavior. In: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, pp. 2646–2651 (2008)
Bastos-Filho, C.J.A., Guimarães, A.C.S.: Multi-objective fish school search. Int. J. Swarm Intell. Res. 6(1), 23–40 (2015)
Madeiro, S.S., de Lima-Neto, F.B., Bastos-Filho, C.J.A., Nascimento Figueiredo, E.M.: Density as the segregation mechanism in fish school search for multimodal optimization problems. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011. LNCS, vol. 6729, pp. 563–572. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21524-7_69
Sargo, J.A.G., Vieira, S.M., Sousa, J.M.C., Bastos-Filho, C.J.A: Binary fish school Search applied to feature selection: Application to ICU readmissions. In: IEEE International Conference on Fuzzy Systems, pp. 1366–1373 (2014)
De Lima-Neto, F.B., Pereira, G., de Lacerda, M.: Weight based fish school search. In: 2014 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 270–277. IEEE (2014)
Monteiro, J.B., Albuquerque, I.M.C., De Lima-Neto, F.B., Ferreira, F.V.S.: Optimizing multi-plateau functions with FSS-SAR (stagnation avoidance routine). In: IEEE-Symposium Series on Computational Intelligence (2016)
Albuquerque, I.M.C., Monteiro, J.B., De Lima-Neto, F.B., Oliveira, A.M.: Solving assembly line balancing problems with fish school search algorithm. In: IEEE-Symposium Series on Computational Intelligence (2016)
Hicks, C.R.: Fundamental Concepts in the Design of Experiments. Holt, Rinehart and Winston, New York (1963)
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
Filho, J.B.M., Albuquerque, I.M.C., Neto, F.B.L., Ferreira, F.V.S. (2017). Improved Search Mechanisms for the Fish School Search Algorithm. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-53480-0_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-53479-4
Online ISBN: 978-3-319-53480-0
eBook Packages: EngineeringEngineering (R0)