Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications
Falahah I.A. Al-Baik O. Alomari S. Bektemyssova G. Gochhait S. Leonova I. Malik O.P. Werner F. Dehghani M.
2024Tech Science Press
Computers, Materials and Continua
2024#79Issue 33631 - 3678 pp.
This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the unique hunting behavior of frilled lizards in their natural habitat. FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards. The algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases: (i) an exploration phase, which mimics the lizard’s sudden attack on its prey, and (ii) an exploitation phase, which simulates the lizard’s retreat to the treetops after feeding. To assess FLO’s efficacy in addressing optimization problems, its performance is rigorously tested on fifty-two standard benchmark functions. These functions include unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions, as well as the challenging CEC 2017 test suite. FLO’s performance is benchmarked against twelve established metaheuristic algorithms, providing a comprehensive comparative analysis. The simulation results demonstrate that FLO excels in both exploration and exploitation, effectively balancing these two critical aspects throughout the search process. This balanced approach enables FLO to outperform several competing algorithms in numerous test cases. Additionally, FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems, further validating its robustness and versatility in solving real-world optimization challenges. Overall, the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.
bio-inspired , engineering , exploitation , exploration , frilled lizard , metaheuristic , Optimization
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Department of Mathematics, Faculty of Science, The Hashemite University, P.O. Box 330127, Zarqa, 13133, Jordan
Department of Software Engineering, Al-Ahliyya Amman University, Amman, 19328, Jordan
Faculty of Science and Information Technology, Software Engineering, Jadara University, Irbid, 21110, Jordan
Department of Computer Engineering, International Information Technology University, Almaty, 050000, Kazakhstan
Symbiosis Institute of Digital and Telecom Management, Constituent of Symbiosis International Deemed University, Pune, 412115, India
Neuroscience Research Institute, Samara State Medical University, Samara, 443001, Russian Federation
Faculty of Social Sciences, Lobachevsky University, Nizhny Novgorod, 603950, Russian Federation
Department of Electrical and Software Engineering, University of Calgary, Calgary, T2N 1N4, AB, Canada
Faculty of Mathematics, Otto-von-Guericke University, P.O. Box 4120, Magdeburg, 39016, Germany
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, 7155713876, Iran
Department of Mathematics
Department of Software Engineering
Faculty of Science and Information Technology
Department of Computer Engineering
Symbiosis Institute of Digital and Telecom Management
Neuroscience Research Institute
Faculty of Social Sciences
Department of Electrical and Software Engineering
Faculty of Mathematics
Department of Electrical and Electronics Engineering
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