Canine Olfactory Optimization (COO): A Surrogate-Assisted Metaheuristic Algorithm Inspired by Canine Olfactory Behavior and Pack Dynamics

Expensive black-box optimization problems, where each function evaluation requires costly simulations or experiments, demand algorithms that balance solution quality with computational efficiency. This paper introduces the Canine Olfactory Optimization (COO) algorithm, a surrogate-assisted metaheuristic inspired by canine olfactory behavior. Dogs possess approximately 300 million olfactory receptors and devote significant neural capacity to scent processing, enabling gradient tracking, spatial scent mapping, and coordinated pack hunting: mechanisms that motivate the proposed optimization framework. COO integrates established techniques within a unified architecture, combining adaptive multi-population search, ensemble-based surrogate modeling, gradient-based local refinement, and a hysteresis-controlled surrogate activation strategy. Rather than proposing entirely new operators, COO emphasizes structured coordination to stabilize surrogate usage and balance exploration-exploitation dynamics. Two complementary studies evaluate performance. Study 1 compares COO with Differential Evolution, Particle Swarm Optimization, Grey Wolf Optimizer, and Whale Optimization Algorithm across 24 benchmark variants (6 functions × 4 transformations: base, shifted, rotated, shifted-rotated) using 10 independent seeds. Study 2 examines artificial neural network hyperparameter optimization on four regression datasets. COO achieves the best efficiency score on the shifted-rotated Schwefel function, a deceptive landscape with distant global optima. It attains the highest observed transformation robustness scores (1.11 for solution quality and 1.42 for efficiency), with modest differences among algorithms. On the Diabetes dataset, COO reduces test MSE by 2.28% relative to DE (2918 vs. 2986). Pareto analysis indicates favorable accuracy-efficiency trade-offs. While not universally dominant, COO demonstrates particular suitability for expensive multimodal black-box problems.