Grasp: Greedy Randomized Adaptive Search Procedures

Abstract

GRASP or greedy randomized adaptive search procedure, is a multistart metaheuristic that repeatedly applies local search starting from solutions constructed by a randomized greedy algorithm. In this article we review the basic building blocks of GRASP. We cover solution construction schemes, local search methods, and the use of path‐relinking as a memory mechanism in GRASP.

Greedy randomized adaptive search procedure with exterior path relinking for differential dispersion minimization

“…Resende and Ribeiro [31,32] present recent and thorough surveys of this method. GRASP is a multi-start methodology where each iteration consists of two stages.…”

A parallel hybrid greedy branch and bound scheme for the maximum distance-2 matching problem

“…The proposed algorithms fall in the category of the Greedy Randomized Adaptive Search Procedures (the GRASP framework) for solving combinatorial optimization problems which have been successfully applied in a great number of application domains (see [18] for a long list of such application domains and the references therein). The first paper on GRASP is [3], while the interested reader can refer to [19] for a detailed and thorough discussion.…”

“…It is well known that greedy heuristics are effective for packing problems [20]. A single major iteration of a Greedy Randomized Adaptive Search Procedure (GRASP) scheme [19] for the (GPP), shown in Fig. 1, would work as follows:…”

A Bin Packing Heuristic for On-Line Service Placement and Performance Control

“…The Frobenius norm, also known as the Euclidean norm, is defined as (3). The Frobenius norm reflects the Euclidean distance of a given matrix, A, from the 0 matrix.…”