Person-Inspired Metaheuristics: From Human Traits to Search Heuristics a Leonardo da Vinci Case Study

Person-inspired metaheuristic algorithms emulate human traits such as curiosity, reasoning, and adaptive learning to balance exploration and exploitation. This study introduces the Vitruvian Man Optimizer (VMO), a Leonardo da Vinciinspired metaheuristic based on balance, proportion, and creative exploration. VMO integrates golden-ratio-based proportional search, rotational and orthogonal perspective shifts, curiositydriven long-range jumps, and local quadratic refinement to improve global and local search performance. The algorithm was evaluated on a quadratic objective and five benchmark functions: Sphere, Rosenbrock, Rastrigin, Ackley, and Booth. Results demonstrate competitive convergence speed, robust stability, and an effective exploration–exploitation balance. VMO was also tested on a six-node Travelling Salesman Problem and compared with MBO and PSO. The results show that VMO achieves near-optimal solutions with strong consistency and computational efficiency, highlighting its potential as a promising metaheuristic optimization approach.