Solving Travelling Salesman Problem with an Improved Hybrid Genetic Algorithm | Zendy

Bao Lin | Zendy; Xiaoyan Sun | Zendy; Sana Salous | Zendy

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Solving Travelling Salesman Problem with an Improved Hybrid Genetic Algorithm

Author(s) -

Bao Lin,

Xiaoyan Sun,

Sana Salous

Publication year - 2016

Publication title -

journal of computer and communications

Language(s) - English

Resource type - Journals

eISSN - 2327-5227

pISSN - 2327-5219

DOI - 10.4236/jcc.2016.415009

Subject(s) - crossover , travelling salesman problem , local optimum , mathematical optimization , local search (optimization) , genetic algorithm , 2 opt , operator (biology) , convergence (economics) , computer science , algorithm , mutation , genetic operator , mathematics , meta optimization , artificial intelligence , biochemistry , chemistry , repressor , transcription factor , economics , gene , economic growth

We present an improved hybrid genetic algorithm to solve the two-dimensional Euclidean\udtraveling salesman problem (TSP), in which the crossover operator is enhanced\udwith a local search. The proposed algorithm is expected to obtain higher\udquality solutions within a reasonable computational time for TSP by perfectly integrating\udGA and the local search. The elitist choice strategy, the local search crossover\udoperator and the double-bridge random mutation are highlighted, to enhance the\udconvergence and the possibility of escaping from the local optima. The experimental\udresults illustrate that the novel hybrid genetic algorithm outperforms other genetic\udalgorithms by providing higher accuracy and satisfactory efficiency in real optimization\udprocessing

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