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.
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