Giant Trevally Optimizer (GTO): A Novel Metaheuristic Algorithm for Global Optimization and Challenging Engineering Problems
Metaheuristic algorithms are becoming powerful methods for solving continuous global optimization and engineering problems due to their flexible implementation on the given problem. Most of these algorithms draw their inspiration from the collective intelligence and hunting behavior of animals in na...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9955508/ |
| _version_ | 1811211618578595840 |
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| author | Haval Tariq Sadeeq Adnan Mohsin Abdulazeez |
| author_facet | Haval Tariq Sadeeq Adnan Mohsin Abdulazeez |
| author_sort | Haval Tariq Sadeeq |
| collection | DOAJ |
| description | Metaheuristic algorithms are becoming powerful methods for solving continuous global optimization and engineering problems due to their flexible implementation on the given problem. Most of these algorithms draw their inspiration from the collective intelligence and hunting behavior of animals in nature. This paper proposes a novel metaheuristic algorithm called the Giant Trevally Optimizer (GTO). In nature, giant trevally feeds on many animals, including fish, cephalopods, and seabirds (sooty terns). In this work, the unique strategies of giant trevally when hunting seabirds are mathematically modeled and are divided into three main steps. In the first step, the foraging movement patterns of giant trevallies are simulated. In the second step, the giant trevallies choose the appropriate area in terms of food where they can hunt for prey. In the last step, the trevally starts to chase the seabird (prey). When the prey is close enough to the trevally, the trevally jumps out of the water and attacks the prey in the air or even snatches the prey from the water surface. The performance of GTO is compared against state-of-the-art metaheuristics for global optimization on a set of forty benchmark functions with different characteristics and five complex engineering problems. The comparative study, scalability analysis, statistical analysis based on the Wilcoxon rank sum test, and the findings suggest that the proposed GTO is an efficient optimizer for global optimization. |
| first_indexed | 2024-04-12T05:15:47Z |
| format | Article |
| id | doaj.art-486f1f3e700e45d6bfa729d774504309 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T05:15:47Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-486f1f3e700e45d6bfa729d7745043092022-12-22T03:46:37ZengIEEEIEEE Access2169-35362022-01-011012161512164010.1109/ACCESS.2022.32233889955508Giant Trevally Optimizer (GTO): A Novel Metaheuristic Algorithm for Global Optimization and Challenging Engineering ProblemsHaval Tariq Sadeeq0https://orcid.org/0000-0002-0998-876XAdnan Mohsin Abdulazeez1Information Technology Department, Technical College of Informatics-Akre, Duhok Polytechnic University, Duhok, IraqEnergy Department, Technical College of Engineering-Duhok, Duhok Polytechnic University, Duhok, IraqMetaheuristic algorithms are becoming powerful methods for solving continuous global optimization and engineering problems due to their flexible implementation on the given problem. Most of these algorithms draw their inspiration from the collective intelligence and hunting behavior of animals in nature. This paper proposes a novel metaheuristic algorithm called the Giant Trevally Optimizer (GTO). In nature, giant trevally feeds on many animals, including fish, cephalopods, and seabirds (sooty terns). In this work, the unique strategies of giant trevally when hunting seabirds are mathematically modeled and are divided into three main steps. In the first step, the foraging movement patterns of giant trevallies are simulated. In the second step, the giant trevallies choose the appropriate area in terms of food where they can hunt for prey. In the last step, the trevally starts to chase the seabird (prey). When the prey is close enough to the trevally, the trevally jumps out of the water and attacks the prey in the air or even snatches the prey from the water surface. The performance of GTO is compared against state-of-the-art metaheuristics for global optimization on a set of forty benchmark functions with different characteristics and five complex engineering problems. The comparative study, scalability analysis, statistical analysis based on the Wilcoxon rank sum test, and the findings suggest that the proposed GTO is an efficient optimizer for global optimization.https://ieeexplore.ieee.org/document/9955508/Giant trevally optimizer (GTO)optimizationmetaheuristicsexplorationexploitationbenchmark functions |
| spellingShingle | Haval Tariq Sadeeq Adnan Mohsin Abdulazeez Giant Trevally Optimizer (GTO): A Novel Metaheuristic Algorithm for Global Optimization and Challenging Engineering Problems IEEE Access Giant trevally optimizer (GTO) optimization metaheuristics exploration exploitation benchmark functions |
| title | Giant Trevally Optimizer (GTO): A Novel Metaheuristic Algorithm for Global Optimization and Challenging Engineering Problems |
| title_full | Giant Trevally Optimizer (GTO): A Novel Metaheuristic Algorithm for Global Optimization and Challenging Engineering Problems |
| title_fullStr | Giant Trevally Optimizer (GTO): A Novel Metaheuristic Algorithm for Global Optimization and Challenging Engineering Problems |
| title_full_unstemmed | Giant Trevally Optimizer (GTO): A Novel Metaheuristic Algorithm for Global Optimization and Challenging Engineering Problems |
| title_short | Giant Trevally Optimizer (GTO): A Novel Metaheuristic Algorithm for Global Optimization and Challenging Engineering Problems |
| title_sort | giant trevally optimizer gto a novel metaheuristic algorithm for global optimization and challenging engineering problems |
| topic | Giant trevally optimizer (GTO) optimization metaheuristics exploration exploitation benchmark functions |
| url | https://ieeexplore.ieee.org/document/9955508/ |
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