20.500.12556/DKUM-96645 Golden drop algorithm: A water wave and golden ratio inspired novel metaheuristic for global Metaheuristic methods are approximate solution techniques that are widely used to solve complex optimization problems. Drawing inspiration from nature, physics, or human behavior, researchers have developed a diverse range of metaheuristic algorithms, either population-based or single-solution-based, with varying neighborhood structures and memory usage. Due to the diversity and complexity of optimization problems in different domains, no single algorithm can be universally effective for all types of problems. This study proposes a novel metaheuristic algorithm, the Golden Drop Algorithm (GDA), which is simple, efficient, and easy to implement. GDA is a physics-inspired method that simulates the propagation of circular waves formed by raindrops falling into water, with wave expansion governed by the golden ratio. The algorithm was evaluated using the Congress on Evolutionary Computation (CEC) 2017 benchmark functions and was compared with three well-known metaheuristics: Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO). The results demonstrate that GDA is not only straightforward and computationally efficient, but also capable of producing competitive and feasible solutions.In 50 % of the CEC 2017 benchmark functions, GDA ranked first, while in 32 % of the functions, it secured second place. In addition, its performance was validated through three benchmark engineering design problems: welded beam design, pressure vessel design, and compression spring design, highlight its robustness and effectiveness in solving constrained nonlinear optimization tasks. metaheuristic optimization physics-inspired metaheuristics water wave golden ratio CEC 2017 benchmark functions engineering design problems metaheuristična prosizvodnja valovi inženisrsko oblikovanje true false false University of Maribor Angleški jezik Slovenski jezik https://apem-journal.org/Archives.html Članek v reviji 2026-01-22 10:42:30 2026-01-22 10:42:31 2026-03-11 11:36:43 0000-00-00 00:00:00 2025 0 0 str. 309-324 no. 3 Vol. 20 Sep. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-08-06 2025-09-15 2025-09-01 658.5 265665795 10.14743/apem2025.3.542 1854-6250 APEM20-3_309-324.pdf APEM20-3_309-324.pdf 1 1683E4A607E03CD1A516085417801A4F 43268daae0d43f8d9817d5a90be018dcef2155984a969e7a0e6c4147a0dcaa1d ea56fcaa-f778-11f0-a060-00155d00018d https://dk.um.si/Dokument.php?lang=slv&id=202393 Fakulteta za strojništvo Advances in production engineering & management 0 0 0