Newton Downhill Optimizer for Global Optimization DOI Creative Commons

Wanting Xiao,

Kaichen Ouyang,

Junbo Lian

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Abstract The study presents the Newton's Downhill Optimizer (NDO), a novel metaheuristic algorithm designed to address challenges of complex, high-dimensional, and nonlinear optimization problems. Mathematical-Based Algorithms (MBAs) are category algorithms based on mathematical principles. They widely applied in numerical computation, symbolic manipulation, geometric processing, problems, probabilistic statistics, offering efficient precise solutions complex Inspired by Method, NDO combines its precision with downhill strategy stochastic processes, specifically real-world applications benchmark method inspired enhancing capability exploring solution space escaping local optima. In tests, demonstrated exceptional performance, surpassing majority competing multiple test suites CEC 2017 2022. We conducted comprehensive comparison against 14 well-established algorithms. These include mathematical-based approaches such as AOA, SCHO, SCA, SABO, NRBO, RUN. also compared it classical like CMA-ES, ABC, DE, PSO. Additionally, we included advanced recently published WSO, EHO, FDB_AGDEand GQPSO. results demonstrate that outperforms most these It exhibits superior convergence speed remarkable stability.In engineering applications, outperformed other reducer design task step-cone pulley delivered outstanding disk clutch brake tasks. A significant contribution is application breast cancer feature selection, tested two Breast datasets. performance accuracy, sensitivity, specificity, Matthews Correlation Coefficient (MCC), achieving accuracy across This underscores potential viable tool for addressing both medical fields. source codes will be shared at https://github.com/oykc1234/NDO.

Язык: Английский

Newton Downhill Optimizer for Global Optimization DOI Creative Commons

Wanting Xiao,

Kaichen Ouyang,

Junbo Lian

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Abstract The study presents the Newton's Downhill Optimizer (NDO), a novel metaheuristic algorithm designed to address challenges of complex, high-dimensional, and nonlinear optimization problems. Mathematical-Based Algorithms (MBAs) are category algorithms based on mathematical principles. They widely applied in numerical computation, symbolic manipulation, geometric processing, problems, probabilistic statistics, offering efficient precise solutions complex Inspired by Method, NDO combines its precision with downhill strategy stochastic processes, specifically real-world applications benchmark method inspired enhancing capability exploring solution space escaping local optima. In tests, demonstrated exceptional performance, surpassing majority competing multiple test suites CEC 2017 2022. We conducted comprehensive comparison against 14 well-established algorithms. These include mathematical-based approaches such as AOA, SCHO, SCA, SABO, NRBO, RUN. also compared it classical like CMA-ES, ABC, DE, PSO. Additionally, we included advanced recently published WSO, EHO, FDB_AGDEand GQPSO. results demonstrate that outperforms most these It exhibits superior convergence speed remarkable stability.In engineering applications, outperformed other reducer design task step-cone pulley delivered outstanding disk clutch brake tasks. A significant contribution is application breast cancer feature selection, tested two Breast datasets. performance accuracy, sensitivity, specificity, Matthews Correlation Coefficient (MCC), achieving accuracy across This underscores potential viable tool for addressing both medical fields. source codes will be shared at https://github.com/oykc1234/NDO.

Язык: Английский

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