Lionfish Search Algorithm: A Novel Nature-Inspired Metaheuristic

Abstract

This study introduces an innovative optimization algorithm called Lionfish Search (LFS) technique, which is inspired by the visual predator Lionfish, in which it is specifically imitating their hunting tactics. The suggested algorithm considers several parameters that influence the hunting behaviour of lionfish, such as visual acuity, mobility, striking success, and prey swallowing potential. Furthermore, this study examines the influence of the physiological traits of the lionfish and their relationship with environmental factors. The novel search algorithm has shown enhanced performance and efficiency, particularly in scenarios where the integration of visual cues and intricate hunting strategies is vital. The suggested LFS method was evaluated using 20 well-known single-modal and multi-modal mathematical functions to analyse its different characteristics. The LFS method has shown remarkable efficacy in both exploration and exploitation, effectively reducing the likelihood of being trapped in local optima. Additionally, it has a rapid convergence capacity, particularly in the realm of large-scale global optimization. Comparisons were made between the LFS algorithm, and 10 other prominent algorithms mentioned in the literature. The proposed LFS metaheuristic algorithm outperformed the others on almost all of the examined functions, demonstrating a statistically significant advantage. Moreover, the positive results found in three practical optimization situations demonstrate the effectiveness of the LFS in accomplishing problem-solving tasks that have limited and unknown search areas.