<p>Stridulating insects, like ants, use acoustic communication as a crucial part of their social behavior, allowing them to coordinate their activities and respond to changing environmental conditions. This paper introduces a novel local search-based approach inspired by the acoustic communication in real ant colonies. Despite the large number of existing bio-inspired metaheuristics, no single algorithm is universally optimal, and a new approach inspired by ant sound communication could offer advantages in specific problems. Sound-based mechanisms could provide better ways to balance exploration and exploitation potentially improve search efficiency. In the proposed approach, artificial ants employ a 2-Opt local search guided by acoustic signals to enhance the quality of a population of initial solutions. To assess the efficacy of the proposed approach, experiments are conducted on various benchmark instances of the traveling salesman problem. Experimental results demonstrate promising performance, outperforming or matching state-of-the-art algorithms on several benchmark instances.</p>