Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems
In this paper, a new bio-inspired metaheuristic algorithm named the Kookaburra Optimization Algorithm (KOA) is introduced, which imitates the natural behavior of kookaburras in nature. The fundamental inspiration of KOA is the strategy of kookaburras when hunting and killing prey. The KOA theory is...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/8/6/470 |
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| author | Mohammad Dehghani Zeinab Montazeri Gulnara Bektemyssova Om Parkash Malik Gaurav Dhiman Ayman E. M. Ahmed |
| author_facet | Mohammad Dehghani Zeinab Montazeri Gulnara Bektemyssova Om Parkash Malik Gaurav Dhiman Ayman E. M. Ahmed |
| author_sort | Mohammad Dehghani |
| collection | DOAJ |
| description | In this paper, a new bio-inspired metaheuristic algorithm named the Kookaburra Optimization Algorithm (KOA) is introduced, which imitates the natural behavior of kookaburras in nature. The fundamental inspiration of KOA is the strategy of kookaburras when hunting and killing prey. The KOA theory is stated, and its mathematical modeling is presented in the following two phases: (i) exploration based on the simulation of prey hunting and (ii) exploitation based on the simulation of kookaburras’ behavior in ensuring that their prey is killed. The performance of KOA has been evaluated on 29 standard benchmark functions from the CEC 2017 test suite for the different problem dimensions of 10, 30, 50, and 100. The optimization results show that the proposed KOA approach, by establishing a balance between exploration and exploitation, has good efficiency in managing the effective search process and providing suitable solutions for optimization problems. The results obtained using KOA have been compared with the performance of 12 well-known metaheuristic algorithms. The analysis of the simulation results shows that KOA, by providing better results in most of the benchmark functions, has provided superior performance in competition with the compared algorithms. In addition, the implementation of KOA on 22 constrained optimization problems from the CEC 2011 test suite, as well as 4 engineering design problems, shows that the proposed approach has acceptable and superior performance compared to competitor algorithms in handling real-world applications. |
| first_indexed | 2024-03-10T21:24:51Z |
| format | Article |
| id | doaj.art-d08030a807d8429fb9c4212cc2598b99 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-10T21:24:51Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-d08030a807d8429fb9c4212cc2598b992023-11-19T15:48:36ZengMDPI AGBiomimetics2313-76732023-10-018647010.3390/biomimetics8060470Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization ProblemsMohammad Dehghani0Zeinab Montazeri1Gulnara Bektemyssova2Om Parkash Malik3Gaurav Dhiman4Ayman E. M. Ahmed5Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, IranDepartment of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, IranDepartment of Computer Engineering, International Information Technology University, Almaty 050000, KazakhstanDepartment of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, LebanonFaculty of Computer Engineering, King Salman International University, El Tor 46511, EgyptIn this paper, a new bio-inspired metaheuristic algorithm named the Kookaburra Optimization Algorithm (KOA) is introduced, which imitates the natural behavior of kookaburras in nature. The fundamental inspiration of KOA is the strategy of kookaburras when hunting and killing prey. The KOA theory is stated, and its mathematical modeling is presented in the following two phases: (i) exploration based on the simulation of prey hunting and (ii) exploitation based on the simulation of kookaburras’ behavior in ensuring that their prey is killed. The performance of KOA has been evaluated on 29 standard benchmark functions from the CEC 2017 test suite for the different problem dimensions of 10, 30, 50, and 100. The optimization results show that the proposed KOA approach, by establishing a balance between exploration and exploitation, has good efficiency in managing the effective search process and providing suitable solutions for optimization problems. The results obtained using KOA have been compared with the performance of 12 well-known metaheuristic algorithms. The analysis of the simulation results shows that KOA, by providing better results in most of the benchmark functions, has provided superior performance in competition with the compared algorithms. In addition, the implementation of KOA on 22 constrained optimization problems from the CEC 2011 test suite, as well as 4 engineering design problems, shows that the proposed approach has acceptable and superior performance compared to competitor algorithms in handling real-world applications.https://www.mdpi.com/2313-7673/8/6/470optimizationbio-inspiredmetaheuristickookaburraexplorationexploitation |
| spellingShingle | Mohammad Dehghani Zeinab Montazeri Gulnara Bektemyssova Om Parkash Malik Gaurav Dhiman Ayman E. M. Ahmed Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems Biomimetics optimization bio-inspired metaheuristic kookaburra exploration exploitation |
| title | Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems |
| title_full | Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems |
| title_fullStr | Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems |
| title_full_unstemmed | Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems |
| title_short | Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems |
| title_sort | kookaburra optimization algorithm a new bio inspired metaheuristic algorithm for solving optimization problems |
| topic | optimization bio-inspired metaheuristic kookaburra exploration exploitation |
| url | https://www.mdpi.com/2313-7673/8/6/470 |
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