Bio-inspired optimization algorithms have long held a prominent position in intelligent optimization research. Traditional methods often draw on mechanisms such as group collaboration, predation, and the immune system, but they still have limitations in balancing exploration and exploitation. This paper proposes an optimization framework based on the measurement principles of pulse oximeters, named the Pulse Oximeter Heuristics with Phase, Dual-Wavelength, and Artifact Rejection (POHPDAR) algorithm. This method abstracts five core mechanisms from the signal acquisition and processing stages of the pulse oximeter: dual-wavelength competition guidance, cardiac phase-locked triggering, perfusion and refractory period scheduling, robust acceptance of motion artifacts, and online Beer–Lambert self-calibration. Through mathematical modeling, the unique aspects of oximeters in optical measurement and signal dynamic response are mapped into key optimization search processes. This paper systematically presents the mathematical derivation and update rules of the algorithm, and analyzes its complexity and theoretical characteristics. This research provides a novel biomedical-inspired approach and opens new avenues for the design of intelligent optimization algorithms.