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@article{185805,
        author = {Samindar J. Vibhute and Chetan S. Arage and Vishal S. Pawar and Abhishek A. Patil},
        title = {Extended Honey Badger Algorithm: A Competitive Metaheuristic for Solving Global Optimization Problems},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {28-38},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185805},
        abstract = {This study proposes the Extended Honey Badger Optimization (EHBO) algorithm, an enhancement of the standard Honey Badger Algorithm (HBA), inspired by the unique predatory behavior of honey badgers. The EHBO introduces adaptive mechanisms designed to balance exploration and exploitation capabilities more effectively. To assess its efficacy, the EHBO is benchmarked against widely-used optimization algorithms, namely Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and the base HBA across standard benchmark functions such as Rastrigin, Rosenbrock, and Sphere functions. Experimental results demonstrate that EHBO outperforms ACO consistently and performs competitively against HBA and PSO, especially on unimodal landscapes like the Sphere function. However, PSO retains superiority across all test cases, particularly on complex multimodal problems. The findings suggest that while EHBO improves upon its base algorithm, further refinement is necessary to rival the performance of mature swarm intelligence algorithms like PSO. The results underscore EHBO’s potential for further development as a robust optimization tool for continuous optimization problems.},
        keywords = {Extended Honey Badger Optimization (EHBO), Swarm Intelligence, Benchmark Functions, Global Optimization},
        month = {},
        }