The Aries Metaheuristic Algorithm: Exploring Global Optimization Through Impulse, Passion, and Adventure

As optimization algorithms are increasingly used in various fields, metaheuristic algorithms have become a research hotspot due to their powerful global optimization capabilities. Inspired by Aries's adventurous spirit, passion, and motivation, this paper proposes a new metaheuristic algorithm, the Aries metaheuristic algorithm (AMA), which aims to optimize the objective function in multidimensional complex problems. This paper elaborates on the design concept, algorithm flow, and characteristics of AMA, and demonstrates the advantages of AMA in global search through experimental verification on classic benchmark functions and practical problems. Finally, compared with traditional algorithms such as particle swarm optimization (PSO), differential evolution (DE), simulated annealing (SA), and random search (Random), AMA has been shown to have superior performance in solving optimization problems. The core innovation of AMA lies in its impulsive search, emotion-driven jumping, and collective cooperation mechanisms, which simulate Aries-like psychological dynamics to guide the global optimization process.