Paper Info
Title
Multi-Direction Searching Ant Colony Optimization for Traveling Salesman Problems
Abstract
Traveling salesman problem (TSP) is one of the most famous NP-hard problems, which has wide application background. Ant colony optimization (ACO) is a nature-inspired algorithm and taken as one of the high performance computing methods for TSP. Classical ACO algorithm like ant colony system (ACS) cannot solve TSP very well. The present paper proposes an ACO algorithm with multi-direction searching capacity to improve the performance in solving TSP. Three weight parameter settings are designed to form a new transition rule, which has multi-direction searching functions in selecting the edges of the TSP tour. The experimental results of solving different kinds of TSP problems indicate the proposed algorithm performs better than the famous ACO algorithm ACS.
Year
DOI
Venue
2008
10.1109/CIS.2008.151
CIS (2)
Keywords
Field
DocType
multidirection searching ant colony optimization,tsp problem,traveling salesman problem,famous np-hard problem,high performance computing method,travelling salesman problems,path routing,tsp tour,ant colony system,ant colony optimization,search problems,computational complexity,proposed algorithm,traveling salesman problems,nature-inspired algorithm,salesman problems,multi-direction searching ant colony,multi-direction searching,aco algorithm,np-hard problems,classical aco algorithm,swarm intelligence,algorithm design and analysis,np hard problem,convergence,construction industry,np hard problems
Convergence (routing),Ant colony optimization algorithms,Mathematical optimization,Algorithm design,Computer science,Swarm intelligence,Travelling salesman problem,Artificial intelligence,Selection rule,Ant colony,Machine learning,Computational complexity theory
Conference
Volume
ISBN
Citations 
2
978-0-7695-3508-1
2
PageRank 
References 
Authors
0.40
9
1
Name
Order
Citations
PageRank
Zhaoquan Cai15212.23