Three-tier multi-agent approach for solving traveling salesman problem

Authors:
Shi-Liang Yan;Ke-Feng Zhou
Affiliations:
Engineering & Technology Center, Southwest University of Science and Technology, Mianyang, P.R. China;Department of computer science, Qiu-Ai Experimental Elementary School, Ningbo, P.R. China
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 7
Cited 0

Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
A New Memetic Algorithm for the Asymmetric Traveling Salesman Problem

Journal of Heuristics
Ant colony system: a cooperative learning approach to the traveling salesman problem

IEEE Transactions on Evolutionary Computation
A hybrid heuristic for the traveling salesman problem

IEEE Transactions on Evolutionary Computation
An evolutionary algorithm for large traveling salesman problems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Solving the traveling salesman problem with annealing-based heuristics: a computational study

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Kohonen-like decomposition method for the Euclidean traveling salesman problem-KNIES_DECOMPOSE

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

The Traveling Salesman Problem (TSP) is a very hard optimization problem in the field of operations research. It has been shown to be NP-hard, and is an often-used benchmark for new optimization techniques. This paper will to bring up a three-tier multi-agent approach for solving the TSP. This proposed approach supports the distributed solving to the TSP. It divides into three-tier (layer), the first tier is ant colony optimization agent and its function is generating the new solution continuously; the second-tier is genetic algorithm agent, its function is optimizing the current solutions group; and the third tier is fast local searching agent and its function is optimizing the best solution from the beginning of the trial. Ultimately, the experimental results have shown that the proposed hybrid approach has good performance with respect to the quality of solution and the speed of computation.