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


Authors: Mitat Uysal, Aynur Uysal

Abstract: 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.

Pages: 1-9

DOI: 10.46300/9109.2027.21.1

International Journal of Education and Information Technologies, E-ISSN: 2074-1316, Volume 21, 2027, Art. #1

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