Alternated Superior Chaotic Ant Colony Optimization in Traveling Salesman Problem

<p>The Traveling Salesman Problem (TSP) is one of the most popular optimization problems.<br>This paper investigates the efficacy of Ant Colony Optimization (ACO) and Chaotic Ant<br>Colony Optimization (CACO) algorithms in addressing the TSP. ACO mimics the foraging<br>behaviour of ants to construct solutions iteratively, while CACO integrates chaotic dynamics<br>to enhance exploration and exploitation capabilities. The research explores the impact of<br>chaotic sequences on optimization algorithms and evaluates their performance through empirical<br>experiments. The research aims to improves the global solutions obtained from CACO<br>by integrating the superior logistic map (SLM) and alternated superior logistic map (ASLM)<br>in CACO. Results demonstrate the effectiveness of both ACO and CACO variants in solving<br>TSP instances, with CACO showcasing improved solution quality and execution time.</p>