## Amazonian Manatee-Inspired Metaheuristic Algorithm for Complex Optimization Problems
Year: 2026
Type: preprint
Abstract: This paper presents a novel metaheuristic algorithm, termed the Amazonian Manatee-Inspired Metaheuristic Algorithm (AMM-MHA), designed to effectively address complex optimization problems. The algorithm's inspiration stems from the unique foraging and navigation strategies employed by Amazonian manatees. The core mechanism of AMM-MHA revolves around simulating the manatee's adaptive movement patterns, incorporating elements such as path exploration, obstacle avoidance, and resource utilization. Specifically, the algorithm leverages a multi-dimensional search space and utilizes a dynamic exploration-exploitation balance guided by the manatee's response to environmental changes. Mathematical formulations are presented to detail the algorithm's key components, including the movement strategy, the adaptive parameter adjustment, and the convergence criteria. The algorithm's performance is evaluated through simulations on a range of benchmark optimization problems, demonstrating its potential for achieving robust and efficient solutions. The results indicate that AMM-MHA offers competitive performance compared to established metaheuristic algorithms, particularly in scenarios requiring adaptability and robustness.
Author: Zhang, Jincheng
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204846649
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