A random-key GRASP for combinatorial optimization
<|reference_start|>A random-key GRASP for combinatorial optimization: This paper proposes a problem-independent GRASP metaheuristic using the random-key optimizer (RKO) paradigm. GRASP (greedy randomized adaptive search procedure) is a metaheuristic for combinatorial optimization that repeatedly applies a semi-greedy construction procedure followed by a local search procedure. The best solution found over all iterations is returned as the solution of the GRASP. Continuous GRASP (C-GRASP) is an extension of GRASP for continuous optimization in the unit hypercube. A random-key optimizer (RKO) uses a vector of random keys to encode a solution to a combinatorial optimization problem. It uses a decoder to evaluate a solution encoded by the vector of random keys. A random-key GRASP is a C-GRASP where points in the unit hypercube are evaluated employing a decoder. We describe random key GRASP consisting of a problem-independent component and a problem-dependent decoder. As a proof of concept, the random-key GRASP is tested on five NP-hard combinatorial optimization problems: traveling salesman problem, tree of hubs location problem, Steiner triple covering problem, node capacitated graph partitioning problem, and job sequencing and tool switching problem.<|reference_end|>
@article{chaves2024a,
    title={A random-key GRASP for combinatorial optimization},
    author={Antonio A. Chaves, Mauricio G.C. Resende, Ricardo M.A. Silva},
    journal={arXiv preprint arXiv:2405.18681},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18681},
    primaryClass={cs.NE cs.AI math.OC}
}