Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems

• Published in: IEEE transactions on cybernetics Vol. 47; no. 7; pp. 1743 - 1756
• Main Authors: Mavrovouniotis, Michalis, Muller, Felipe M., Shengxiang Yang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Ant colony optimization; Ant colony optimization (ACO); Benchmark testing; dynamic traveling salesman problem (DTSP); Generators; Heuristic algorithms; Inserts; local search; memetic algorithm; Memetics; Performance enhancement; Searching; State of the art; Travel time; Traveling salesman problem; Traveling salesman problems; Urban areas
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2016.2556742

For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to tackle such problems due to their adaptation capabilities. It has been shown that the integration of local search operators can significantly improve the performance of ACO. In this paper, a memetic ACO algorithm, where a local search operator (called unstring and string) is integrated into ACO, is proposed to address DTSPs. The best solution from ACO is passed to the local search operator, which removes and inserts cities in such a way that improves the solution quality. The proposed memetic ACO algorithm is designed to address both symmetric and asymmetric DTSPs. The experimental results show the efficiency of the proposed memetic algorithm for addressing DTSPs in comparison with other state-of-the-art algorithms.