Gannet optimization algorithm: A new metaheuristic algorithm for solving engineering optimization problems

Jeng-Shyang Pan, Li-Gang Zhang, Ruo-Bin Wang, Václav Snášel and Shu-Chuan Chu

Mathematics and Computers in Simulation (MATCOM), 2022, vol. 202, issue C, 343-373

Abstract: Engineering design problems are usually large-scale constrained optimization problems, and metaheuristic algorithms are vital for solving such complex problems. Therefore, this paper introduces a new nature-inspired metaheuristic algorithm: the gannet optimization algorithm (GOA). The GOA mathematizes the various unique behaviors of gannets during foraging and is used to enable exploration and exploitation. GOA’s U-shaped and V-shaped diving patterns are responsible for exploring the optimal region within the search space, with sudden turns and random walks ensuring better solutions are found in this region. In order to verify the ability of the GOA to find the optimal solution, we compared it with other comparison algorithms in multiple dimensions of 28 benchmark functions. We found that the GOA has a shorter running time in high dimensions and can provide a better solution. Finally, we apply the GOA to five engineering optimization problems. The experimental results show that the GOA is suitable for many constrained engineering design problems and can provide better solutions in most cases.

Keywords: Gannet optimization algorithm; Optimization algorithms; Swarm intelligence; Evolutionary computation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:202:y:2022:i:c:p:343-373

DOI: 10.1016/j.matcom.2022.06.007

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