Open Access iconOpen Access

ARTICLE

crossmark

Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications

Ibraheem Abu Falahah1, Osama Al-Baik2, Saleh Alomari3, Gulnara Bektemyssova4, Saikat Gochhait5,6, Irina Leonova7, Om Parkash Malik8, Frank Werner9,*, Mohammad Dehghani10

1 Department of Mathematics, Faculty of Science, The Hashemite University, P.O. Box 330127, Zarqa, 13133, Jordan
2 Department of Software Engineering, Al-Ahliyya Amman University, Amman, 19328, Jordan
3 Faculty of Science and Information Technology, Software Engineering, Jadara University, Irbid, 21110, Jordan
4 Department of Computer Engineering, International Information Technology University, Almaty, 050000, Kazakhstan
5 Symbiosis Institute of Digital and Telecom Management, Constituent of Symbiosis International Deemed University, Pune, 412115, India
6 Neuroscience Research Institute, Samara State Medical University, Samara, 443001, Russia
7 Faculty of Social Sciences, Lobachevsky University, Nizhny Novgorod, 603950, Russia
8 Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
9 Faculty of Mathematics, Otto-von-Guericke University, P.O. Box 4120, Magdeburg, 39016, Germany
10 Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, 7155713876, Iran

* Corresponding Author: Frank Werner. Email: email

(This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)

Computers, Materials & Continua 2024, 79(3), 3631-3678. https://doi.org/10.32604/cmc.2024.053189

Abstract

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.

Keywords


Cite This Article

APA Style
Falahah, I.A., Al-Baik, O., Alomari, S., Bektemyssova, G., Gochhait, S. et al. (2024). Frilled lizard optimization: A novel bio-inspired optimizer for solving engineering applications. Computers, Materials & Continua, 79(3), 3631-3678. https://doi.org/10.32604/cmc.2024.053189
Vancouver Style
Falahah IA, Al-Baik O, Alomari S, Bektemyssova G, Gochhait S, Leonova I, et al. Frilled lizard optimization: A novel bio-inspired optimizer for solving engineering applications. Comput Mater Contin. 2024;79(3):3631-3678 https://doi.org/10.32604/cmc.2024.053189
IEEE Style
I.A. Falahah et al., "Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving Engineering Applications," Comput. Mater. Contin., vol. 79, no. 3, pp. 3631-3678. 2024. https://doi.org/10.32604/cmc.2024.053189



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 439

    View

  • 142

    Download

  • 0

    Like

Share Link