Deer Hunting Optimization Algorithm: A New Nature-Inspired Meta-heuristic Paradigm

TLDR

Deer hunting strategies rely on hunters’ developed tactics for attacking the buck. This paper introduces DHOA, a novel meta‑heuristic algorithm inspired by human deer‑hunting behavior. DHOA models two hunters—leader and successor—who iteratively update their positions toward a target, and it is evaluated on 39 benchmark functions, 3 engineering problems, and a neural‑network‑augmented classification task. Experimental results show that DHOA outperforms or matches state‑of‑the‑art algorithms such as GWO, WOA, FF, and PSO on benchmarks and real‑time engineering applications.

Abstract

This paper proposes a novel meta-heuristic algorithm, named DHOA, which is inspired by the hunting behavior of humans toward deer. Even though the activities of the hunters differ, the way of attacking the buck/deer is based on the hunting strategy they develop. The hunting strategy depends on the movement of two hunters in their best positions, termed as leader and successor. Accordingly, each hunter updates his position until they reach the buck. The experimental results reveal that the proposed DHOA provides competitive results when compared with the state-of-the-art optimization algorithms, such as GWO, WOA, FF, PSO, etc. The experimentation is carried out with 39 benchmark functions and 3 engineering applications. Moreover, a specific application is exploited by integrating NN in DHOA (DHOA-NN), to show the efficiency of the proposed algorithm in the classification. The proposed algorithm experimented in real-time engineering applications and the performance comparison with the existing optimization algorithms proves the superiority of the DHOA algorithm.