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Improved genetic algorithms for the travelling salesman problem

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  • Zakir Hussain Ahmed

Abstract

The travelling salesman problem (TSP) is a benchmark problem in which a salesman has to visit all nodes (cities) in a network exactly once except the starting node, come back to the starting node and find the shortest tour. Genetic algorithm (GA) is one of the best algorithms to deal with the travelling salesman problem (TSP). In GA, crossover operator plays a vital role and the sequential constructive crossover (SCX) is one of the best crossover operators for solving the TSP. Several improvements have been proposed for other crossover operators. In this paper we propose four improved genetic algorithms using three local search methods - 2-opt search, a hybrid mutation, and a combined mutation operator, and incorporate them into SCX. The experimental results on some TSPLIB instances show that our improved GAs can significantly improve simple GA using SCX in terms of solution quality.

Suggested Citation

  • Zakir Hussain Ahmed, 2014. "Improved genetic algorithms for the travelling salesman problem," International Journal of Process Management and Benchmarking, Inderscience Enterprises Ltd, vol. 4(1), pages 109-124.
    • Handle: RePEc:ids:ijpmbe:v:4:y:2014:i:1:p:109-124
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    Cited by:

    1. Zakir Hussain Ahmed, 2016. "Experimental analysis of crossover and mutation operators on the quadratic assignment problem," Annals of Operations Research, Springer, vol. 247(2), pages 833-851, December.

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