A Novel Nature-Inspired Optimization Algorithm: Grizzly Bear Fat Increase Optimizer
This paper introduces a novel nature-inspired optimization algorithm called the Grizzly Bear Fat Increase Optimizer (GBFIO). The GBFIO algorithm mimics the natural behavior of grizzly bears as they accumulate body fat in preparation for winter, drawing on their strategies of hunting, fishing, and ea...
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MDPI AG
2025-06-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/6/379 |
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| author | Moslem Dehghani Mokhtar Aly Jose Rodriguez Ehsan Sheybani Giti Javidi |
| author_facet | Moslem Dehghani Mokhtar Aly Jose Rodriguez Ehsan Sheybani Giti Javidi |
| author_sort | Moslem Dehghani |
| collection | DOAJ |
| description | This paper introduces a novel nature-inspired optimization algorithm called the Grizzly Bear Fat Increase Optimizer (GBFIO). The GBFIO algorithm mimics the natural behavior of grizzly bears as they accumulate body fat in preparation for winter, drawing on their strategies of hunting, fishing, and eating grass, honey, etc. Hence, three mathematical steps are modeled and considered in the GBFIO algorithm to solve the optimization problem: (1) finding food sources (e.g., vegetables, fruits, honey, oysters), based on past experiences and olfactory cues; (2) hunting animals and protecting offspring from predators; and (3) fishing. Thirty-one standard benchmark functions and thirty CEC2017 test benchmark functions are applied to evaluate the performance of the GBFIO, such as unimodal, multimodal of high dimensional, fixed dimensional multimodal, and also the rotated and shifted benchmark functions. In addition, four constrained engineering design problems such as tension/compression spring design, welded beam design, pressure vessel design, and speed reducer design problems have been considered to show the efficiency of the proposed GBFIO algorithm in solving constrained problems. The GBFIO can successfully solve diverse kinds of optimization problems, as shown in the results of optimization of objective functions, especially in high dimension objective functions in comparison to other algorithms. Additionally, the performance of the GBFIO algorithm has been compared with the ability and efficiency of other popular optimization algorithms in finding the solutions. In comparison to other optimization algorithms, the GBFIO algorithm offers yields superior or competitive quasi-optimal solutions relative to other well-known optimization algorithms. |
| format | Article |
| id | doaj-art-c6869d0120c84fd9a8a3e367257ddb11 |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-06-01 |
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| series | Biomimetics |
| spelling | doaj-art-c6869d0120c84fd9a8a3e367257ddb112025-08-20T03:26:15ZengMDPI AGBiomimetics2313-76732025-06-0110637910.3390/biomimetics10060379A Novel Nature-Inspired Optimization Algorithm: Grizzly Bear Fat Increase OptimizerMoslem Dehghani0Mokhtar Aly1Jose Rodriguez2Ehsan Sheybani3Giti Javidi4Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Bellavista 7, Santiago 8420524, ChileFacultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Bellavista 7, Santiago 8420524, ChileFacultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Bellavista 7, Santiago 8420524, ChileSchool of Information Systems and Management, Muma College of Business, University of South Florida, Tampa, FL 33620, USASchool of Information Systems and Management, Muma College of Business, University of South Florida, Tampa, FL 33620, USAThis paper introduces a novel nature-inspired optimization algorithm called the Grizzly Bear Fat Increase Optimizer (GBFIO). The GBFIO algorithm mimics the natural behavior of grizzly bears as they accumulate body fat in preparation for winter, drawing on their strategies of hunting, fishing, and eating grass, honey, etc. Hence, three mathematical steps are modeled and considered in the GBFIO algorithm to solve the optimization problem: (1) finding food sources (e.g., vegetables, fruits, honey, oysters), based on past experiences and olfactory cues; (2) hunting animals and protecting offspring from predators; and (3) fishing. Thirty-one standard benchmark functions and thirty CEC2017 test benchmark functions are applied to evaluate the performance of the GBFIO, such as unimodal, multimodal of high dimensional, fixed dimensional multimodal, and also the rotated and shifted benchmark functions. In addition, four constrained engineering design problems such as tension/compression spring design, welded beam design, pressure vessel design, and speed reducer design problems have been considered to show the efficiency of the proposed GBFIO algorithm in solving constrained problems. The GBFIO can successfully solve diverse kinds of optimization problems, as shown in the results of optimization of objective functions, especially in high dimension objective functions in comparison to other algorithms. Additionally, the performance of the GBFIO algorithm has been compared with the ability and efficiency of other popular optimization algorithms in finding the solutions. In comparison to other optimization algorithms, the GBFIO algorithm offers yields superior or competitive quasi-optimal solutions relative to other well-known optimization algorithms.https://www.mdpi.com/2313-7673/10/6/379optimizationmetaheuristicnature-inspiredbenchmark test functionsgrizzly bear fat increase optimizer |
| spellingShingle | Moslem Dehghani Mokhtar Aly Jose Rodriguez Ehsan Sheybani Giti Javidi A Novel Nature-Inspired Optimization Algorithm: Grizzly Bear Fat Increase Optimizer Biomimetics optimization metaheuristic nature-inspired benchmark test functions grizzly bear fat increase optimizer |
| title | A Novel Nature-Inspired Optimization Algorithm: Grizzly Bear Fat Increase Optimizer |
| title_full | A Novel Nature-Inspired Optimization Algorithm: Grizzly Bear Fat Increase Optimizer |
| title_fullStr | A Novel Nature-Inspired Optimization Algorithm: Grizzly Bear Fat Increase Optimizer |
| title_full_unstemmed | A Novel Nature-Inspired Optimization Algorithm: Grizzly Bear Fat Increase Optimizer |
| title_short | A Novel Nature-Inspired Optimization Algorithm: Grizzly Bear Fat Increase Optimizer |
| title_sort | novel nature inspired optimization algorithm grizzly bear fat increase optimizer |
| topic | optimization metaheuristic nature-inspired benchmark test functions grizzly bear fat increase optimizer |
| url | https://www.mdpi.com/2313-7673/10/6/379 |
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