Aye-Aye Optimizer (AAO): A Bio-Inspired Metaheuristic Algorithm Based on the Percussive Foraging Strategy

Abstract This paper introduces the Aye-Aye Optimizer (AAO) as a novel bio-inspired metaheuristic algorithm that mimics the unique percussive foraging strategy of the Aye-Aye (Daubentonia madagascariensis), a nocturnal lemur native to Madagascar. In nature, the Aye-Aye uses rhythmic tapping to detect hidden prey beneath tree bark, followed by precise localization using auditory cues and final extraction with its elongated middle finger and strong incisors. Inspired by this hunting behavior, the AAO models the optimization process in three sequential phases: (1) exploratory percussion, where random tapping patterns enable a broad search of the solution space; (2) focused localization, where the sensitivity and tapping radius adaptively decrease to refine promising regions; and (3) exploitation and extraction, where the optimizer performs deep local search to extract the global optimum. The transition of these phases would be performed through three new defined parameters namely alpha, beta, and gamma. To validate the performance of the AAO, extensive experiments are conducted on 23 benchmark test functions, as well as several real-world engineering design problems, and functions from the CEC2022 competition. The obtained results demonstrate that the AAO achieves an effective balance between exploration and exploitation, shows faster convergence, and delivers superior robustness compared to well-established metaheuristics. The AAO establishes a new paradigm of intelligence, demonstrating how complex adaptive behavior can emerge from a simple biological principle.