Recently, metaheuristic algorithms have become a very interesting research field due to their ability to address complex and diverse problems. This paper presents a novel metaheuristic called Narwhal Optimizer (NO) inspired by narwhals behaviors. The NO algorithm mimics the hunting mechanism of narwhals. The narwhals are marine mammals known for their sophisticated communication based on clicks sound to locate their prey. The algorithm is based on three main steps: signal emission, signal propagation, and position updating of the narwhals. The hunting process, which is based on signal emission and propagation, is formulated as an optimization algorithm. The strategies observed in narwhal pods are emulated to enhance exploration and exploitation in the search space. The NO algorithm is benchmarked on 13 well-known functions, including unimodal, multimodal, and fixed-dimension multimodal functions. The experimental results showed that NO provides satisfactory and reasonable solutions in terms of avoiding local minima and achieving global optimality.

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