Abstract
In this study, a new optimization algorithm called the Hitchcock Birds Inspired Algorithm (HBIA) is introduced, inspired by the aggressive bird behavior portrayed by Alfred Hitchcock in the 1963 thriller “The Birds”. When gathering elements about the phenomenon of birds throughout the film, it is possible to enumerate characteristics of the behavior of the birds that Hitchcock portrayed in the film. HBIA is a stochastic swarm intelligence algorithm that captures the essence of the fictional behavior of birds exposed by Hitchcock and model an optimization mechanism. The algorithm was based on the attack pattern of birds in the film, which has the stages of stalking, attack and reorganization, defined by the initialization, movement strategies in the search space and strategy of local minimum escape, respectively. The technique has as differential the use of adaptive parameters, a discretized random initialization and the use of the Beta distribution. When comparing to SCA, WOA, TLBO and VS, HBIA’s performance is investigated by several experiments implemented in eight cost functions. The results show that the HBIA can find more satisfactory solutions in high dimensionality in the majority of the evaluated cost functions compared to the other four methods.
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References
Hitchcock, A.: The Birds [Motion Picture]. United States, Universal Studios (1963)
Shields, W.M.: Barn swallow mobbing: self-defence, collateral kin defence, group defence or parental care? Anim. Behav. 32, 132–148 (1984)
Evans, M., et al.: Statistical Distributions, 3rd edn. Wiley, New York (2000)
Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. The Morgan Kaufmann Series in Artificial Intelligence. Morgan Kaufmann, San Diego (2001)
Rao, R., Savsani, V., Vakharia, D.: Teaching learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput.-Aided Des. 43(3), 303–315 (2011)
Jamil, M., Yang, X.: A literature survey of benchmark functions for global optimization problems. Int. J. Math. Model. Numer. Optim. 4(2), 150–194 (2013)
Silva, D., Maroldi, A., Lima, L.: Outliers na lei do elitismo. Revista da Faculdade de Biblioteconomia e Comunicação da UFRGS (2014)
Doan, B., Olmez, T.: A new metaheuristic for numerical function optimization: Vortex search algorithm. Inf. Sci. 293, 125–145 (2015)
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)
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Morais, R.G., Mourelle, L.M., Nedjah, N. (2018). Hitchcock Birds Inspired Algorithm. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11056. Springer, Cham. https://doi.org/10.1007/978-3-319-98446-9_16
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DOI: https://doi.org/10.1007/978-3-319-98446-9_16
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