Ant Colony Optimization with Genetic Operation and Its Application to Traveling Salesman Problem

Rong-Long WANG  Xiao-Fan ZHOU  Kozo OKAZAKI  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E92-A   No.5   pp.1368-1372
Publication Date: 2009/05/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E92.A.1368
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Numerical Analysis and Optimization
Keyword: 
ant colony optimization,  combinatorial optimization problems,  traveling salesman problem,  genetic algorithm,  genetic operation,  

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Summary: 
Ant colony optimization (ACO) algorithms are a recently developed, population-based approach which has been successfully applied to optimization problems. However, in the ACO algorithms it is difficult to adjust the balance between intensification and diversification and thus the performance is not always very well. In this work, we propose an improved ACO algorithm in which some of ants can evolve by performing genetic operation, and the balance between intensification and diversification can be adjusted by numbers of ants which perform genetic operation. The proposed algorithm is tested by simulating the Traveling Salesman Problem (TSP). Experimental studies show that the proposed ACO algorithm with genetic operation has superior performance when compared to other existing ACO algorithms.