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Al-Baik O, Alomari S, Alssayed O, Gochhait S, Leonova I, Dutta U, Malik OP, Montazeri Z, Dehghani M. Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems. Biomimetics (Basel) 2024; 9:65. [PMID: 38392111 PMCID: PMC10887113 DOI: 10.3390/biomimetics9020065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 01/10/2024] [Accepted: 01/18/2024] [Indexed: 02/24/2024] Open
Abstract
A new bio-inspired metaheuristic algorithm named the Pufferfish Optimization Algorithm (POA), that imitates the natural behavior of pufferfish in nature, is introduced in this paper. The fundamental inspiration of POA is adapted from the defense mechanism of pufferfish against predators. In this defense mechanism, by filling its elastic stomach with water, the pufferfish becomes a spherical ball with pointed spines, and as a result, the hungry predator escapes from this threat. The POA theory is stated and then mathematically modeled in two phases: (i) exploration based on the simulation of a predator's attack on a pufferfish and (ii) exploitation based on the simulation of a predator's escape from spiny spherical pufferfish. The performance of POA is evaluated in handling the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that POA has achieved an effective solution with the appropriate ability in exploration, exploitation, and the balance between them during the search process. The quality of POA in the optimization process is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that POA provides superior performance by achieving better results in most of the benchmark functions in order to solve the CEC 2017 test suite compared to competitor algorithms. Also, the effectiveness of POA to handle optimization tasks in real-world applications is evaluated on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. Simulation results show that POA provides effective performance in handling real-world applications by achieving better solutions compared to competitor algorithms.
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Affiliation(s)
- Osama Al-Baik
- Department of Software Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan
| | - Saleh Alomari
- ISBM COE, Faculty of Science and Information Technology, Software Engineering, Jadara University, Irbid 21110, Jordan
| | - Omar Alssayed
- Department of Mathematics, Faculty of Science, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan
| | - Saikat Gochhait
- Symbiosis Institute of Digital and Telecom Management, Constituent of Symbiosis International Deemed University, Pune 412115, India
- Neuroscience Research Institute, Samara State Medical University, 89 Chapaevskaya Street, 443001 Samara, Russia
| | - Irina Leonova
- Neuroscience Research Institute, Samara State Medical University, 89 Chapaevskaya Street, 443001 Samara, Russia
- Faculty of Social Sciences, Lobachevsky University, 603950 Nizhny Novgorod, Russia
| | - Uma Dutta
- Former Dean of Life Sciences and Head of Zoology Department, Celland Molecular Biology, Toxicology Laboratory, Department of Zoology, Cotton University, Guwahati 781001, India
| | - Om Parkash Malik
- Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Zeinab Montazeri
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran
| | - Mohammad Dehghani
- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran
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