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Chen, S.-M. and Chien, C.-Y. (2011) Solving the Traveling Salesman Problem Based on the Genetic Simulated Annealing Ant Colony System with Particle Swarm Optimization Techniques. Expert Systems with Applications, 38, 14439-14450.
https://doi.org/10.1016/j.eswa.2011.04.163

has been cited by the following article:

• TITLE: Solving the Traveling Salesman Problem Using Hydrological Cycle Algorithm

  AUTHORS: Ahmad Wedyan, Jacqueline Whalley, Ajit Narayanan

  KEYWORDS: Water-Based Optimization Algorithms, Nature-Inspired Computing, Discrete Optimization Problems, NP-Hard Problems

  JOURNAL NAME: American Journal of Operations Research, Vol.8 No.3, May 25, 2018

  ABSTRACT: In this paper, a recently developed nature-inspired optimization algorithm called the hydrological cycle algorithm (HCA) is evaluated on the traveling salesman problem (TSP). The HCA is based on the continuous movement of water drops in the natural hydrological cycle. The HCA performance is tested on various geometric structures and standard benchmarks instances. The HCA has successfully solved TSPs and obtained the optimal solution for 20 of 24 benchmarked instances, and near-optimal for the rest. The obtained results illustrate the efficiency of using HCA for solving discrete domain optimization problems. The solution quality and number of iterations were compared with those of other metaheuristic algorithms. The comparisons demonstrate the effectiveness of the HCA.