Authors A.V. Martinov, V.M. Kureichik
Month, Year 04, 2015 @en
Index UDC 004.023
Abstract The purpose of this work is to develop an effective hybrid method for solving the traveling salesman problem based on evolutionary and swarm techniques. Ant colony optimization and genetic algorithms are alternatives for solving discrete optimization problems. Genetic algorithm is a heuristic search algorithm used for solving optimization problems and modeling by random selection, combinations and variations of the unknown parameters using mechanisms similar to natural selection in nature, and the ant colony optimization, in turn, uses a decentralized selforganizing behavioral tools of ant colony which search the optimal route in the graph model. This paper presents a combination study for genetic algorithm and ant colony optimization applied in the travelling salesman problem. This hybridization is not only successively use of genetic algorithm and ant colony optimization, but also integrating the genetic information in ant colony optimization selection path rule. The genetic algorithm operators used for recombination of candidate solutions obtained in the course of the ant colony optimization algorithm. A heuristic evaluation of the solutions found by agents of ant algorithm to further their selection genetic algorithm is presented. In the course of this work was developed computer programs that implements the algorithm described above. A comparison of the test results and the hybrid ant algorithms on international benchmarks is presented. The results obtained in the experiments showed that hybrid algorithm searches solution higher quality than the conventional ant algorithm.

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Keywords Ant colony optimization; genetic algorithm; swarm intelligence; traveling salesman problem; discrete optimization.
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