An improved ant colony optimization algorithm with embedded genetic algorithm for the traveling salesman problem

An improved ant colony optimization algorithm with embedded genetic algorithm for the traveling salesman problem

In this paper we proposed an improved ant colony optimization algorithm with embedded genetic algorithm to solve the traveling salesman problem. The main idea is to let genetic algorithm simulate the consulting mechanism, which may have more chances to find a better solution, to optimize the solutio...

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Hauptverfasser: Fanggeng Zhao, Jinyan Dong, Sujian Li, Jiangsheng Sun
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Sprache:eng
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Algorithm design and analysis
Ant colony optimization
Cities and towns
Gallium
Genetic algorithm
Genetic algorithms
Optimization
Traveling salesman problem
Traveling salesman problems
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Jinyan Dong
Sujian Li
Jiangsheng Sun
description In this paper we proposed an improved ant colony optimization algorithm with embedded genetic algorithm to solve the traveling salesman problem. The main idea is to let genetic algorithm simulate the consulting mechanism, which may have more chances to find a better solution, to optimize the solutions found by the ants. In the proposed algorithm, we employed a new greedy way of solution construction and designed an improved crossover operator for consultation in the embedded genetic algorithm. Experimental results showed that the proposed algorithm could find better solutions of benchmark instances within fewer iterations than existing ant colony algorithms.
doi_str_mv 10.1109/WCICA.2008.4594163
format Conference Proceeding
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subjects Algorithm design and analysis
Ant colony optimization
Cities and towns
Gallium
Genetic algorithm
Genetic algorithms
Optimization
Traveling salesman problem
Traveling salesman problems
title An improved ant colony optimization algorithm with embedded genetic algorithm for the traveling salesman problem
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