@Article{AJ_2024.12.03.OR.04,
    doi     = { 10.17093/alphanumeric.1506894 },
    author  = { Mehmet Fatih Demiral },
    title   = { Analysis of the Computational Performance in Traveling Salesman Problem: An Application of the Grey Prediction Hybrid Black Hole Algorithm },
    abstract = { Grey prediction evolution algorithm (GPEA) is a nature-inspired intelligent approach applied to global optimization and
    engineering problems in 2020. The performance of the GPEA is evaluated on benchmark functions, global optimization, and
    tested on six engineering-constrained design problems. The comparison shows the effectiveness and superiority of the
    GPEA. Although the pure GPEA is better than other algorithms in global optimization, and engineering problems, it shows
    poor performance in combinatorial optimization. In this work, GPEA hybridizes with the black hole algorithm and tabu
    search for the event horizon condition. Besides, the grey prediction hybrid black hole algorithm (GPHBH) is implemented
    with heuristics, such as 2-opt, 3-opt, and k-opt swap, and tries to improve with constructive heuristics, such as NN
    (nearest neighbor), and k-NN. All the algorithms have been tested under appropriate parameters in this work. The
    traveling salesman problem has been used as a benchmark problem so eight benchmark OR-Library datasets are experimented
    with. The experimental solutions are presented as best, average solutions, standard deviation, and CPU time for all
    datasets. As a result, GPHBH and its derived forms give alternative and acceptable solutions to combinatorial
    optimization in admissible CPU time. }
    journal = { Alphanumeric Journal },
    year    = { 2024 },
    volume  = { 12 },
    number  = { 3 },
    pages   = { 281-292 },
    url     = { https://alphanumericjournal.com/article/analysis-of-the-computational-performance-in-traveling-salesman-problem-an-application-of-the-grey-prediction-hybrid-black-hole-algorithm },
}