1,218 research outputs found
Genetic Algorithm with Optimal Recombination for the Asymmetric Travelling Salesman Problem
We propose a new genetic algorithm with optimal recombination for the
asymmetric instances of travelling salesman problem. The algorithm incorporates
several new features that contribute to its effectiveness: (i) Optimal
recombination problem is solved within crossover operator. (ii) A new mutation
operator performs a random jump within 3-opt or 4-opt neighborhood. (iii)
Greedy constructive heuristic of W.Zhang and 3-opt local search heuristic are
used to generate the initial population. A computational experiment on TSPLIB
instances shows that the proposed algorithm yields competitive results to other
well-known memetic algorithms for asymmetric travelling salesman problem.Comment: Proc. of The 11th International Conference on Large-Scale Scientific
Computations (LSSC-17), June 5 - 9, 2017, Sozopol, Bulgari
APPLICATION OF GENETIC ALGORITHMS TO THE TRAVELING SALESMAN PROBLEM
The purpose of this paper was to investigate in practice the possibility of using evolutionary algorithms to solve the traveling salesman problem on a real example. The goal was achieved by developing an original implementation of the evolutionary algorithm in Python, and by preparing an example of the traveling salesman problem in the form of a directed graph representing polish voivodship cities. As part of the work an application in Python was written. It provides a user interface which allows setting selected parameters of the evolutionary algorithm and solving the prepared problem. The results are presented in both text and graphical form. The correctness of the evolutionary algorithm's operation and the implementation was confirmed by performed tests. A large number of tested solutions (2500) and the analysis of the obtained results allowed for a conclusion that an optimal (relatively suboptimal) solution had been found
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