TY - EJOU AU - Masadeh, Raja AU - Almomani, Omar AU - Zaqebah, Abdullah AU - Masadeh, Shayma AU - Alshqurat, Kholoud AU - Sharieh, Ahmad AU - Alsharman, Nesreen TI - Narwhal Optimizer: A Nature-Inspired Optimization Algorithm for Solving Complex Optimization Problems T2 - Computers, Materials \& Continua PY - 2025 VL - 85 IS - 2 SN - 1546-2226 AB - This research presents a novel nature-inspired metaheuristic optimization algorithm, called the Narwhale Optimization Algorithm (NWOA). The algorithm draws inspiration from the foraging and prey-hunting strategies of narwhals, “unicorns of the sea”, particularly the use of their distinctive spiral tusks, which play significant roles in hunting, searching prey, navigation, echolocation, and complex social interaction. Particularly, the NWOA imitates the foraging strategies and techniques of narwhals when hunting for prey but focuses mainly on the cooperative and exploratory behavior shown during group hunting and in the use of their tusks in sensing and locating prey under the Arctic ice. These functions provide a strong assessment basis for investigating the algorithm’s prowess at balancing exploration and exploitation, convergence speed, and solution accuracy. The performance of the NWOA is evaluated on 30 benchmark test functions. A comparison study using the Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Perfumer Optimization Algorithm (POA), Candle Flame Optimization (CFO) Algorithm, Particle Swarm Optimization (PSO) Algorithm, and Genetic Algorithm (GA) validates the results. As evidenced in the experimental results, NWOA is capable of yielding competitive outcomes among these well-known optimizers, whereas in several instances. These results suggest that NWOA has proven to be an effective and robust optimization tool suitable for solving many different complex optimization problems from the real world. KW - Optimization; metaheuristic optimization algorithm; narwhal optimization algorithm; benchmarks DO - 10.32604/cmc.2025.066797