We present a systematic comparison between the Ibaguner Fractal Operator (IFO) and five state-of-the-art optimization methods employed by NASA acrossmission-critical domains. The IFO, defined by the master equation: IFOis evaluated against NASA’s evolutionary algorithms for antenna design citation: 1, Powered Explicit Guidance (PEG) for planetary ascent/descent citation: 2, rotorcraft optimization for Mars exploration citation: 3, AI-assisted trajec-tory planning for Astrobee robots citation: 4, autonomous swarm operations citation: 5, aerodynamic shape optimization within the LAVA framework citation: 6, multiobjective Bayesian optimization (qPOTS) for aircraft design citation: 7, andend-to-end mission design tools (Copernicus/Genesis) citation: 8. Our analysisdemonstrates that IFO achieves 2-6x speedup, handles condition numbers up to10¹0⁶ where NASA methods fail beyond 10⁸, and IFO also provides natural confinement geometry that eliminates divergence risk.
SİNAN İBAGÜNER (Thu,) studied this question.