Abstract
Ebb-Tide-Fish Algorithm (ETFA) is a simple but powerful optimization algorithm over continuous search spaces, and the inspiration comes from the foraging behavior of the fish in ebb tide. This kind of fish is a fascinating creature, and it often draws my attention when I walk on the beach. When I studied and got an idea of improving some optimization algorithms recently, the kind of fish flashes in my mind. The algorithm mainly focuses on the diversity of locations of the fish rather than what velocity it is when the fish swim from the current location to a better one. The algorithm gives a formulation of the foraging behavior of the fish, and the detailed model is also given in the paper. The performance of ETFA on a testbed of four functions is compared with several famous published methods. The final results show that ETFA has a faster convergence rate with an excellent accuracy.
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References
Storn, R., Price, K.: Differential evolutionCa simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)
Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer, US (2010)
Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 65–74. Springer, Heidelberg (2010)
Puranik, P., Bajaj, P., Abraham, A., Palsodkar, P., Deshmukh, A.: Human Perception-based Color Image Segmentation Using Comprehensive Learning Particle Swarm Optimization. Journal of Information Hiding and Multimedia Signal Processing 2(2), 227–235 (2011)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)
Engelbrecht, A.P.: Fundamentals of computational swarm intelligence. John Wiley & Sons (2006)
Chu, S.-C., Tsai, P.-w., Pan, J.-S.: Cat swarm optimization. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS (LNAI), vol. 4099, pp. 854–858. Springer, Heidelberg (2006)
Neshat, M., Sepidnam, G., Sargolzaei, M., et al.: Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artificial Intelligence Review, 1–33 (2012)
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Meng, Z., Pan, JS. (2015). A Simple and Accurate Global Optimizer for Continuous Spaces Optimization. In: Sun, H., Yang, CY., Lin, CW., Pan, JS., Snasel, V., Abraham, A. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 329. Springer, Cham. https://doi.org/10.1007/978-3-319-12286-1_13
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DOI: https://doi.org/10.1007/978-3-319-12286-1_13
Publisher Name: Springer, Cham
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