A Novel Beagle Inspired Optimization Algorithm: Comprehensive Evaluation on Benchmarking Functions

This paper presents an evaluation of the novel Beagle-Inspired Optimization Algorithm (BIOA), inspired by the scent detection and rabbit hunting strategies of beagle dogs, such as scent detection, tracking, trail following, pattern recognition, continuous adaptation, persistent and exhaustive search, and escape and retrieval. BIOA is compared with well-established algorithms, including Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), and Cuckoo Search (CS), across a set of benchmark functions, including Sphere, Rosenbrock, Rastrigin, Griewank, Ackley, Levy, and Schwefel functions. The results demonstrate BIOA's superior performance, achieving the lowest mean fitness values and best solutions across most test cases. Its balanced exploration and exploitation phases enable effective optimization. While BIOA excels in many instances, it requires further improvements in computational efficiency, particularly for high-dimensional problems. Future research should focus on enhancing BIOA's performance through advanced models, hybrid optimization techniques, and real-world problem applications, thus broadening its practical impact in solving complex optimization tasks.