Ant Colony Optimization with Genetic Operation and Its Application to Traveling Salesman Problem
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概要
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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 1ntensification 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.
著者
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WANG Rong-Long
Faculty of Engineering, Fukui University
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ZHOU Xiao-Fan
Faculty of Engineering, University of Fukui
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OKAZAKI Kozo
Faculty of Engineering, University of Fukui
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