Abstract
A better understanding of natural behavior modeling in mathematical systems has enabled a new class of stochastic optimization algorithms that can estimate optimal solutions using reasonable computational resources for problems where exact algorithms show poor performance. The position up-dating mechanism in various optimization algorithms utilizes similar chaotic random behavior which impedes the performance of the search for a globally optimum solution in monotonic nonlinear search space. In this work, an approach is proposed that tackles these issues on an already established algorithm; Improved Barnacle Mating Optimization (IBMO) Algorithm, inspired by the movement and mating of Gooseneck Barnacles. The algorithm introduces the mimicry of the movement and mating behavior in nature to model an optimization process. Several benchmark functions are employed to gauge the performance of the proposed optimization technique. Results are compared with several meta-heuristics and conventional optimization algorithms. It is observed that the IBMO algorithm performs generally better and provides a huge potential for solving real-world problems.
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This work was supported by Top Research Centre Mechatronics (TRCM), University of Agder (UiA), Norway.
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Raza Moosavi, S.K., Zafar, M.H., Mirjalili, S., Sanfilippo, F. (2023). Improved Barnacles Movement Optimizer (IBMO) Algorithm for Engineering Design Problems. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2023. Lecture Notes in Computer Science(), vol 14125. Springer, Cham. https://doi.org/10.1007/978-3-031-42505-9_36
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