A random-key GRASP for combinatorial optimization
CoRR(2024)
摘要
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.
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