Abstract
In the past few years, CUDA and OpenCL are developed in full use of the GPU, which is a significant topic in high performance computing. In this paper, we have proposed an implementation of the genetic algorithm for the traveling salesman problem on the parallel OpenCL architecture. Population initialization, fitness evaluation, selection, crossover and mutation operators are implemented on the GPU by using individual to thread mapping. Moreover we have evaluated our algorithm using a set of benchmark instances from the TSPLIB library. The comparison results shows that GPU computations provide better performance than traditional CPU implementation.
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Zhang, K., Yang, S., li, L., Qiu, M. (2014). Parallel Genetic Algorithm with OpenCL for Traveling Salesman Problem. In: Pan, L., Păun, G., Pérez-Jiménez, M.J., Song, T. (eds) Bio-Inspired Computing - Theories and Applications. Communications in Computer and Information Science, vol 472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45049-9_96
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DOI: https://doi.org/10.1007/978-3-662-45049-9_96
Publisher Name: Springer, Berlin, Heidelberg
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