Abstract
Honey Badger algorithm (HBA) is a recently developed metaheuristic algorithm inspired by the intelligent foraging behavior of Honey Badgers where its digging and honey-finding strategies are simulated in the exploration and exploitation of the search space. However, HBA may suffer from slow convergence speed, unbalanced exploration and exploitation, and local optima stagnation problems. To overcome these limitations, we proposed a modified variant of HBA (LOLSHBA) where Lens Opposition Based Learning is used at the initialization phase to enhance the population quality and diversity and better exploration. The exponential decay function is used as a density factor for accelerating the global convergence speed and for controlling the smooth time-varying transition from exploration to the exploitation phase. A time-varying acceleration constant is also incorporated in the density factor to maintain the balance of local searching and global convergence by emulating a natural reflex action in the movement of honey badgers and guiding the hunting process in each iteration. Finally, to increase exploitation, the Local Search Algorithm is used to boost the search in the local neighborhood of the global best solution at the end of every iteration. For performance evaluation, we utilized well-known benchmark functions where the proposed LOLSHBA performs better in 12 out of 19 functions and 26 out of 29 IEEE CEC 2017 functions. Statistical analyses also validate the superior performance of LOLSHBA. Besides, LOLSHBA is also tested for problem-solving which proves that LOLSHBA is more suitable to handle constrained engineering design problems compared to other optimization algorithms.
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Open source benchmark data sets are used.
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Conceptualization: PM and SM. Formal Analysis: SM and PM. Investigation and Methodology: DB and PM. Data Curation and Software: DB and PM. Validation: PM and DB. Visualization: SM and DB. Writing–Original Draft: PM. Writing Review & Editing: SM and DB. Supervision: DB and SM.
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Appendix
Appendix
1.1 IEEE CEC 2017 test suite analysis
IEEE CEC 2017 is a highly complex test suite that can be used to assess the quality of LOLSHBA. There are 29 benchmark functions comprising of unimodal (F1 and F3), multimodal (F4–F10), hybrid (F11-F20), and composite (F21–F30) functions (Wu et al. 2017). Table 26 show the mean and standard deviation (STD) of the optimal fitness values achieved by the algorithms. The P-values at \(\alpha\) = 0.05 obtained from the Friedman test are shown in Table 27. It can be seen that LOLSHBA outperformed the majority of the algorithms where the best results are shown in bold font. HHO outperforms others in the case of F9. HBA outperforms others in the case of F20. ESSAWOA outperforms others in the case of F24. The multimodal functions are used to evaluate the ability to avoid local regions. While the composite functions with highly dynamic search spaces are used to investigate the balance between the exploration and exploitation capabilities of the search algorithms.
As the results interpret, LOLSHBA outperformed other metaheuristics on 26 out of 29 benchmark functions in IEEE CEC 2017 functions. The best results are highlighted in boldface. These results suggest that the proposed LOLSHBA algorithm can solve various optimization problems effectively.
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Majumdar, P., Mitra, S. & Bhattacharya, D. Honey Badger algorithm using lens opposition based learning and local search algorithm. Evolving Systems 15, 335–360 (2024). https://doi.org/10.1007/s12530-023-09495-z
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DOI: https://doi.org/10.1007/s12530-023-09495-z