  This is a comprehensive review of the Greedy Randomized Adaptive Search
Procedure (GRASP) metaheuristic and its hybridization with Path Relinking (PR)
over the past two decades. GRASP with PR has become a widely adopted approach
for solving hard optimization problems since its proposal in 1999. The paper
covers the historical development of GRASP with PR and its theoretical
foundations, as well as recent advances in its implementation and application.
The review includes a critical analysis of variants of PR, including
memory-based and randomized designs, with a total of ten different
implementations. It describes these advanced designs both theoretically and
practically on two well-known optimization problems, linear ordering and
max-cut. The paper also explores the hybridization of GRASP with PR and other
metaheuristics, such as Tabu Search and Scatter Search. Overall, this review
provides valuable insights for researchers and practitioners seeking to utilize
GRASP with PR for solving optimization problems.
