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

Fanggeng Zhao, Jinyan Dong, Sujian Li, Jiangsheng Sun

Abstract

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.

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Available abstract

Key concepts: Travelling salesman problem, Crossover, Ant colony optimization algorithms, Benchmark (surveying), Greedy algorithm, Genetic algorithm, Algorithm, Computer science