Ant colony optimization and greedy algorithm performance comparison in travelling salesman problem

Nestled within the intricate realm of combinatorial optimization, the TSP emerges as a focal point, not merely entangled in theoretical complexities but also exerting a substantial influence on the practical terrains of logistics and supply chain management. This thesis embarks on a comparative journey, intricately exploring the efficacies of two prominent optimization methodologies—ACO and the Greedy Algorithm—in unraveling the subtleties of the TSP. Recognizing the pivotal role of streamlined route planning in modern supply chains, the study meticulously examines the theoretical underpinnings and practical ramifications of these algorithms. The methodological framework for this study encompasses the execution and assessment of ACO and the Greedy Algorithm through Matlab programming. The algorithms are meticulously executed within the Matlab environment, allowing for a precise examination of their performance. The comparative tests encompass a set of meticulously crafted scenarios for evaluating the algorithms' efficiency in addressing the TSP. The Matlab platform serves as the computational engine, enabling the execution of algorithmic processes and facilitating an in-depth analysis of key metrics, including time and cost. The choice of Matlab as the programming environment ensures a standardized and rigorous comparison, providing a robust foundation for deriving meaningful insights into the capabilities and limitations of both ACO and the Greedy Algorithm. The study yields illuminating results by closely examining pivotal metrics, such as time and cost, providing a well-rounded comprehension of the performance dynamics of ACO and the Greedy Algorithm across a spectrum of TSP scenarios. ACO's versatility shines through in its adept navigation of diverse TSP instances, while the Greedy Algorithm's efficiency becomes apparent in optimizing more extended and intricate routes without significant drawbacks. This comparative exploration introduces and clarifies methodologies and outcomes, contributing significantly to the discourse on optimization strategies for real-world logistical challenges. Considering the intricate complexity of global supply chains, the selection between ACO and the Greedy Algorithm is crucial for streamlined solutions for the TSP. The thesis aims to offer actionable insights for practitioners and researchers seeking optimal algorithmic approaches in the expansive field of logistics and supply chain optimization.