Farmer Ants Optimization Algorithm: A Novel Metaheuristic for Solving Discrete Optimization Problems
Currently, certain complex issues are classified as NP-hard problems, for which there is no exact solution, or they cannot be solved in a reasonable amount of time. As a result, metaheuristic algorithms have been developed as an alternative. These algorithms aim to approximate the optimal solution r...
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MDPI AG
2025-03-01
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| author | Ali Asghari Mahdi Zeinalabedinmalekmian Hossein Azgomi Mahmoud Alimoradi Shirin Ghaziantafrishi |
| author_facet | Ali Asghari Mahdi Zeinalabedinmalekmian Hossein Azgomi Mahmoud Alimoradi Shirin Ghaziantafrishi |
| author_sort | Ali Asghari |
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| description | Currently, certain complex issues are classified as NP-hard problems, for which there is no exact solution, or they cannot be solved in a reasonable amount of time. As a result, metaheuristic algorithms have been developed as an alternative. These algorithms aim to approximate the optimal solution rather than providing a definitive one. Over recent years, these algorithms have gained considerable attention from the research community. Nature and its inherent principles serve as the primary inspiration for the development of metaheuristic algorithms. A notable subgroup of these algorithms is evolutionary algorithms, which are modeled based on the behavior of social and intelligent animals and organisms. However, each metaheuristic algorithm typically excels only with specific types of problems. As a result, researchers continuously endeavor to develop new algorithms. This study introduces a novel metaheuristic algorithm known as the Farmer Ants Optimization Algorithm (FAOA). The algorithm is inspired by the life of farmer ants, which cultivate mushrooms for food, protect them from pests, and nourish them as they grow. These behaviors, based on their social dynamics, serve as the foundation for the proposed algorithm. Experiments conducted on various engineering and classical problems have demonstrated that the FAOA provides acceptable solutions for discrete optimization problems. |
| format | Article |
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| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-94c3eb0830544358bb4787f7c144d4b82025-08-20T02:11:23ZengMDPI AGInformation2078-24892025-03-0116320710.3390/info16030207Farmer Ants Optimization Algorithm: A Novel Metaheuristic for Solving Discrete Optimization ProblemsAli Asghari0Mahdi Zeinalabedinmalekmian1Hossein Azgomi2Mahmoud Alimoradi3Shirin Ghaziantafrishi4Department of Computer Engineering, Shafagh Institute of Higher Education, Tonekabon 4683165363, IranDepartment of Computer Engineering, Shafagh Institute of Higher Education, Tonekabon 4683165363, IranDepartment of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht 413353516, IranDepartment of Computer Engineering, Shafagh Institute of Higher Education, Tonekabon 4683165363, IranMinistry of Education, Mashhad 9133714165, IranCurrently, certain complex issues are classified as NP-hard problems, for which there is no exact solution, or they cannot be solved in a reasonable amount of time. As a result, metaheuristic algorithms have been developed as an alternative. These algorithms aim to approximate the optimal solution rather than providing a definitive one. Over recent years, these algorithms have gained considerable attention from the research community. Nature and its inherent principles serve as the primary inspiration for the development of metaheuristic algorithms. A notable subgroup of these algorithms is evolutionary algorithms, which are modeled based on the behavior of social and intelligent animals and organisms. However, each metaheuristic algorithm typically excels only with specific types of problems. As a result, researchers continuously endeavor to develop new algorithms. This study introduces a novel metaheuristic algorithm known as the Farmer Ants Optimization Algorithm (FAOA). The algorithm is inspired by the life of farmer ants, which cultivate mushrooms for food, protect them from pests, and nourish them as they grow. These behaviors, based on their social dynamics, serve as the foundation for the proposed algorithm. Experiments conducted on various engineering and classical problems have demonstrated that the FAOA provides acceptable solutions for discrete optimization problems.https://www.mdpi.com/2078-2489/16/3/207metaheuristic algorithmsfarmer ants optimization algorithmoptimization |
| spellingShingle | Ali Asghari Mahdi Zeinalabedinmalekmian Hossein Azgomi Mahmoud Alimoradi Shirin Ghaziantafrishi Farmer Ants Optimization Algorithm: A Novel Metaheuristic for Solving Discrete Optimization Problems Information metaheuristic algorithms farmer ants optimization algorithm optimization |
| title | Farmer Ants Optimization Algorithm: A Novel Metaheuristic for Solving Discrete Optimization Problems |
| title_full | Farmer Ants Optimization Algorithm: A Novel Metaheuristic for Solving Discrete Optimization Problems |
| title_fullStr | Farmer Ants Optimization Algorithm: A Novel Metaheuristic for Solving Discrete Optimization Problems |
| title_full_unstemmed | Farmer Ants Optimization Algorithm: A Novel Metaheuristic for Solving Discrete Optimization Problems |
| title_short | Farmer Ants Optimization Algorithm: A Novel Metaheuristic for Solving Discrete Optimization Problems |
| title_sort | farmer ants optimization algorithm a novel metaheuristic for solving discrete optimization problems |
| topic | metaheuristic algorithms farmer ants optimization algorithm optimization |
| url | https://www.mdpi.com/2078-2489/16/3/207 |
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