Particle swarm optimization for traveling salesman problem

By Kangping Wang, Lan Huang, Chunguang Zhou, Wei Pang. Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), 2003

442 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0ac88b2ac25b5f37e9e891c223262e1e5b38d569

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Henry Shu-Hung Chung
2. Yu-hui Shi
3. Jun Zhang
4. Chun Lu
5. Guangyu Zhu
6. H. P. Lee
7. Yanchun Liang
8. Wei-neng Chen

In Control and Systems Engineering

1. Henry Shu-Hung Chung
2. Yu-hui Shi
3. Jun Zhang
4. Guangyu Zhu
5. Wei-neng Chen
6. Weigang Wu
7. Wen-liang Zhong
8. Wei-Bo Zhang

In Electrical and Electronic Engineering

1. Guangyu Zhu
2. Henry Shu-Hung Chung
3. Wei-Bo Zhang

Citations in the corpus

• Particle swarm optimization-based algorithms for TSP and generalized TSP (2007, methodology): “Wang et al. [17] redefined the PSO operators by introducing the concepts of “Swap operator” and “Swap sequence”, therefore the TSP problems could be solved by the PSO in another way.”
• A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems (2010, methodology): “A similar approach was also proposed by Wang et al. [ 11 ] for the traveling salesman problem (TSP).”
• Drilling path optimization by the particle swarm optimization algorithm with global convergence characteristics (2008, methodology): “On the basis of the existing research achievements (Wang et al. 2003, Lopes and Coelho 2005, Clerc 2006) of PSO applied in TSP and the mathematical model of PSO, a new global convergence PSO fit for drilling path optimization is developed in this paper.”