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

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 mathematiz...

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Vydané v:	Mathematics and computers in simulation Ročník 202; s. 343 - 373
Hlavní autori:	Pan, Jeng-Shyang, Zhang, Li-Gang, Wang, Ruo-Bin, Snášel, Václav, Chu, Shu-Chuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.12.2022
Predmet:	Evolutionary computation Gannet optimization algorithm Optimization algorithms Swarm intelligence Optimization algorithms Swarm intelligence Gannet optimization algorithm Evolutionary computation
ISSN:	0378-4754, 1872-7166
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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.
AbstractList	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.
Author	Snášel, Václav Zhang, Li-Gang Chu, Shu-Chuan Wang, Ruo-Bin Pan, Jeng-Shyang
Author_xml	– sequence: 1 givenname: Jeng-Shyang orcidid: 0000-0002-3128-9025 surname: Pan fullname: Pan, Jeng-Shyang email: jengshyangpan@gmail.com organization: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China – sequence: 2 givenname: Li-Gang orcidid: 0000-0001-5287-017X surname: Zhang fullname: Zhang, Li-Gang email: lgangzhang@126.com organization: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China – sequence: 3 givenname: Ruo-Bin surname: Wang fullname: Wang, Ruo-Bin email: robin945@163.com organization: School of Information Science and Technology, North China University of Technology, Beijing, China – sequence: 4 givenname: Václav orcidid: 0000-0002-9600-8319 surname: Snášel fullname: Snášel, Václav email: vaclav.snasel@vsb.cz organization: Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, Ostrava, Czech Republic – sequence: 5 givenname: Shu-Chuan orcidid: 0000-0003-2117-0618 surname: Chu fullname: Chu, Shu-Chuan email: scchu0803@gmail.com organization: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
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SubjectTerms	Evolutionary computation Gannet optimization algorithm Optimization algorithms Swarm intelligence
Title	Gannet optimization algorithm : A new metaheuristic algorithm for solving engineering optimization problems
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