Three-Body Deterministic Optimizer TBD

Deterministic optimizer from three-body dynamics and logistic chaos; Newtonian/Lagrangian moves for robust engineering design & ML tuning.

Three-Body Deterministic Optimizer (TBD) is a purely deterministic global optimizer inspired by classical three-body dynamics with a logistic-map chaotic driver. It alternates Newtonian and Lagrangian maneuvers to refine candidate solutions within bounds. TBD has demonstrated strong performance on engineering design problems, machine learning hyperparameter tuning, and benchmark test suites such as CEC2017 and CEC2022.

Features

• Deterministic search (no stochastic randomness required)
• Logistic-map chaos to diversify exploration
• Dual maneuvers: Newtonian (best-orbit pull) & Lagrangian (peer-body)
• Validated on CEC2017 and CEC2022 benchmarks
• Applied to engineering design optimization and ML hyperparameter tuning
• Tracks best value, best vector, full trajectory, and final population states