The Marangoni Effect Algorithm: A Novel Physics-Inspired Metaheuristic for Global Optimization
Abstract
Abstract This paper introduces the Marangoni Effect Algorithm (MEA), a novel physics-inspired metaheuristic for continuous global optimization. MEA mimics the surface tension-driven flow phenomenon---known as the Marangoni effect---which involves two distinct phases: droplet spreading and aggregation. The algorithm employs a swarm-based search mechanism where the spreading radius adaptively decreases over iterations, balancing exploration and exploitation. To evaluate MEA's performance, 23 benchmark functions from the widely recognized Yao's 23 benchmark functions---including unimodal, multimodal, and fixed-dimension problems---are employed. The results are compared with four well-established metaheuristics: Big Bang-Big Crunch (BBBC), Black Hole (BH), Ant Lion Optimizer (ALO), and Grey Wolf Optimizer (GWO). Extensive statistical analysis, including mean and standard deviation over 50 independent runs, demonstrates that MEA outperforms in the majority of test cases in terms of convergence speed, solution accuracy, and robustness. Notably, MEA achieves the global optimum with zero standard deviation for several complex multimodal functions (F16, F18, F21, F22, and F23), confirming its exceptional reliability. The algorithm's simple parameter tuning and rapid convergence make it a promising tool for solving challenging optimization problems in engineering and applied sciences.
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