Abstract
Ant Colony Optimization (ACO) algorithm is one of the effective solutions to solve the problem of combination optimization like traveling salesman problem (TSP) which belongs to NP-hard problem. However, this algorithm is robust and has a strong ability for solution discovery, but the convergence speed of that is low and stuck into local optimum. Therefore, for overcoming the drawbacks of ACO, we proposed a self-adaptive ACO with unique strategies to improve uncertain convergence time and random decisions of this algorithm. The proposed technique (DEACO) adjusting the ACO parameters dynamically. In this mechanism, main idea is how to select the first city (start point) to achieve the shortest path based on clustering. In this approach, DEACO finds the minimum cost/shortest path for each cluster. The data that used for this experiment is from TSPLIB library under MATLAB simulation with 10 TSP instances. The experiment outcome illustrates better performance of the proposed method than the conventional ACO in term of faster convergence speed and higher search accuracy.
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