| Iclal Gor A New Variant of the Brown Bear Optimizer Driven with Chebyshev Chaotic Map Approximation |
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| Abstract. This work proposes a novel variation of the Brown Bear Optimization Algorithm (BOA) by integrating Chebyshev chaotic map into its design. The Brown Bear Optimization Algorithm, while capable of maintaining a balance of exploration and exploitation, may suffer from low population diversity and premature convergence, particularly in multimodal search spaces. These issues can lead to suboptimal solutions and reduced global search capability. To evaluate the effectiveness of this modification, Chebyshev chaotic map is used. The performance of the improved algorithm is evaluated using some different types of standard benchmark functions and compared to the original BOA. The experimental results suggest that the integrating Chebyshev chaotic map may offer improvements in the solution quality of the algorithm. The results highlight the potential of chaos-based strategies to improve the exploration capacity of the metaheuristic algorithms. |
| Keywords: global optimization; metaheuristics; nature-inspired algorithm; brown bear optimization; chaotic map; benchmark test function |
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