TY - GEN
T1 - Cooperative Co-evolutionary Metaheuristics for Solving Large-Scale TSP Art Project
AU - Chen, Junfeng
AU - Wang, Yuhao
AU - Xue, Xingsi
AU - Cheng, Shi
AU - El-Abd, Mohammed
N1 - Chen J., Wang Y., Xue X., Cheng S., and El-Abd M. (2019). Cooperative Co-evolutionary Metaheuristics for Solving Large-Scale TSP Art Project. IEEE Symposium Series on Computational Intelligence. 2706-2713.
PY - 2019/12
Y1 - 2019/12
N2 - As the amount and scale of cities in Traveling Salesman Problem (TSP) rise, the algorithmic complexity is exponentially increasing. The difficulty is how to design a suitable algorithm to solve large-scale TSPs. A Cooperative Co-evolutionary Ant Colony Optimization algorithm (CC-ACO) is proposed in this paper based on the concept of divide and conquer. The Iterative Self-Organizing Data Analysis (ISODATA) clustering algorithm tackles the problem by dividing it into a set of smaller and simpler sub-components and the ACO algorithms are designed for optimizing them separately. Numerical tests are then conducted to investigate algorithms, analyze results, and compare performances. The simulation findings show a significant efficiency of the suggested algorithm on the TSPLIB data set. Finally, we extend the large-scale TSP problem to the field of art and use the presented algorithm to optimize the path of discrete pixels in the picture, showing the artistic painting of the TSP art project.
AB - As the amount and scale of cities in Traveling Salesman Problem (TSP) rise, the algorithmic complexity is exponentially increasing. The difficulty is how to design a suitable algorithm to solve large-scale TSPs. A Cooperative Co-evolutionary Ant Colony Optimization algorithm (CC-ACO) is proposed in this paper based on the concept of divide and conquer. The Iterative Self-Organizing Data Analysis (ISODATA) clustering algorithm tackles the problem by dividing it into a set of smaller and simpler sub-components and the ACO algorithms are designed for optimizing them separately. Numerical tests are then conducted to investigate algorithms, analyze results, and compare performances. The simulation findings show a significant efficiency of the suggested algorithm on the TSPLIB data set. Finally, we extend the large-scale TSP problem to the field of art and use the presented algorithm to optimize the path of discrete pixels in the picture, showing the artistic painting of the TSP art project.
KW - ant colony optimization
KW - cooperative coevolution
KW - iterative self-organizing data analysis
KW - large-scale traveling salesman problem
UR - http://www.scopus.com/inward/record.url?scp=85080965876&partnerID=8YFLogxK
U2 - 10.1109/SSCI44817.2019.9002754
DO - 10.1109/SSCI44817.2019.9002754
M3 - Conference contribution
T3 - 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
SP - 2706
EP - 2713
BT - 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
Y2 - 6 December 2019 through 9 December 2019
ER -