Ant Colony Optimization with Memory and Its Application to Traveling Salesman Problem

Rong-Long WANG  Li-Qing ZHAO  Xiao-Fan ZHOU  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E95-A   No.3   pp.639-645
Publication Date: 2012/03/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E95.A.639
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Numerical Analysis and Optimization
ant colony optimization,  memory,  combinatorial optimization problems,  traveling salesman problem,  

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Ant Colony Optimization (ACO) is one of the most recent techniques for solving combinatorial optimization problems, and has been unexpectedly successful. Therefore, many improvements have been proposed to improve the performance of the ACO algorithm. In this paper an ant colony optimization with memory is proposed, which is applied to the classical traveling salesman problem (TSP). In the proposed algorithm, each ant searches the solution not only according to the pheromone and heuristic information but also based on the memory which is from the solution of the last iteration. A large number of simulation runs are performed, and simulation results illustrate that the proposed algorithm performs better than the compared algorithms.