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    A COMPARATIVE STUDY ON PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHMS FOR TRAVELING SALESMAN PROBLEMS
    (TAYLOR & FRANCIS INC, 2009) Cunkas, Mehmet; Ozsaglam, M. Yasin
    This article deals with a performance evaluation of particle swarm optimization (PSO) and genetic algorithms (GA) for traveling salesman problem (TSP). This problem is known to be NP-hard, and consists of the solution containing N! permutations. The objective of the study is to compare the ability to solve the large-scale and other benchmark problems for both algorithms. All simulation has been performed using a software program developed in the Delphi environment. As yet, overall results show that genetic algorithms generally can find better solutions compared to the PSO algorithm, but in terms of average generation it is not good enough.