Design and Use of the CPAN Branch & Bound for the Solution of the Travelling Salesman Problem (TSP)
CONIELECOMP '05 Proceedings of the 15th International Conference on Electronics, Communications and Computers
An Improved Immune-Genetic Algorithm for the Traveling Salesman Problem
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
DisABC: A new artificial bee colony algorithm for binary optimization
Applied Soft Computing
A Novel Constructive-Optimizer Neural Network for the Traveling Salesman Problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Match twice and stitch: a new TSP tour construction heuristic
Operations Research Letters
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Because the traveling salesman problem (TSP) is one type of classical NP-hard problems, it is not easy to find the optimal tour in polynomial time. Some conventional deterministic methods and exhaustive algorithms are applied to small-scale TSP; whereas, heuristic algorithms are more advantageous for the large-scale TSP. Inspired by the behavior of honey bee swarm, Artificial Bee Colony (ABC) algorithms have been developed as potential optimization approaches and performed well in solving scientific researches and engineering applications. This paper proposes two efficient ABC algorithms with heuristic swap operators (i.e., ABC-HS1 and ABC-HS2) for TSP, which are used to search its better tour solutions. A series of numerical experiments are arranged between the proposed two ABC algorithms and the other three ABC algorithms for TSP. Experimental results demonstrate that ABC-HS1 and ABC-HS2 are both effective and efficient optimization methods.