A novel bionic optimization algorithm inspired by mountain goats’ natural strategies of risk assessment and energy management is presented. The model employs a dedicated risk-avoidance coefficient and adaptive energy dynamics to balance global exploration with focused local search. The method was evaluated on the Sphere, Rosenbrock, Rastrigin and Ackley functions and compared against PSO and DE. Experimental results demonstrate fast and stable convergence on smooth landscapes and competitive performance on complex multimodal problems. Overall, the approach proves effective and reliable for high-dimensional optimization tasks.
| Mualliflar | Abdurazzoqov, J.R., Абдураззоков, Дж.Р., Abdurazzoqov, J.R. |
|---|---|
| Jurnal | Ilim ha’m ja’miyet |
| Nashr sanasi | 2026-02-16 |
| Son | 1-1 |
| Betlar | 6-10 |
| Til | uz_Latn |
biologik optimallashtirish, metaevristika, evolyutsion algoritm, xavf mexanizmi, energiya modeli, konvergensiya, биологическая оптимизация, метаэвристика, эволюционный алгоритм, механизм риска, энергетическая модель, сходимость, biological optimization, metaheuristics, evolutionary algorithm, risk mechanism, energy model, convergence
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