Adaptive Neighborhood Search’s DGSO Applied to Travelling Saleman Problem
In order to further study the effectiveness and applicability of the glowworm swarm optimization algorithm, this paper proposed a discrete glowworm swarm optimization algorithm with an adaptive neighborhood search, and used it to solve traveling salesman problem (TSP). Based on the analysis and optimization of the different genetic operations, a new adaptive DGSO algorithm is presented (ADGSO), which is effective for both local search and global search. And we defined a new kind of glowworm, which can adjust the flight length of particles by self-adapting. By solving the different instances of TSP, experimental results indicate that ADGSO has a remarkable quality of the global convergence reliability and convergence velocity. It solved the problems of traditional DGSO algorithm “premature”. Unlike existing TSP approaches that often aggregate multiple criteria and constraints into a compromise function, the proposed ADGSO optimizes all routing constraints and objectives simultaneously, which improves the routing solutions in many aspects, such as lower routing cost, wider scattering area and better convergence trace. The ADGSO is applied to solve the TSP, which yields solutions better than or competitive as compared to the best solutions published in literature.
KeywordsADGSO Local optimization operator Adaptive TSP
This project was supported by National Natural Science Foundation of China (Grant No. 61179032), and the Graduate Innovation Fund of Wuhan Polytechnic University (2014cx007). In addition, we would also thank every authors appeared in the references.
- 4.Zhou, K., Tong, X.J., Xu, J.: Closed circle DNA algorithm of change positive-weighted hamilton circuit problem. J. Syst. Eng. Electron. 20, 636–642 (2009)Google Scholar
- 6.Zhou, Y.Q., Huang, Z.X., Liu, H.X.: Discrete glowworm swarm optimization algorithm for TSP problems. Acta Electronica Sin. 40, 1164–1170 (2012)Google Scholar
- 11.Yao, M.H., Wang, N., Zhao, L.P.: Improved simulated annealing algorithm and genetic algorithm for TSP. Comput. Eng. Appl. 49(14), 60–65 (2013)Google Scholar
- 12.Zhong, Y.W., Yang, J.G., Ning, Z.Y.: Discrete particle swarm optimization algorithm for TSP problem. Syst. Eng. Theory Pract. 26(6), 88–94 (2006)Google Scholar
- 13.He, Y., Liu, G.Y.: Research on solving TSP in tabu search algorithm. J. Southwest China Normal Univ. 27(3), 341–345 (2002)Google Scholar
- 17.Zhang, X., Pan, L., Paun, A.: On the universality of axon P systems. IEEE Trans. Neural Networks Learn. Syst. (2015). doi: 10.1109/TNNLS.2015.2396940