Results 1 
7 of
7
GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURES
, 2002
"... GRASP is a multistart metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phas ..."
Abstract

Cited by 595 (81 self)
 Add to MetaCart
GRASP is a multistart metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phase. The best overall solution is kept as the result. In this chapter, we first describe the basic components of GRASP. Successful implementation techniques and parameter tuning strategies are discussed and illustrated by numerical results obtained for different applications. Enhanced or alternative solution construction mechanisms and techniques to speed up the search are also described: Reactive GRASP, cost perturbations, bias functions, memory and learning, local search on partially constructed solutions, hashing, and filtering. We also discuss in detail implementation strategies of memorybased intensification and postoptimization techniques using pathrelinking. Hybridizations with other metaheuristics, parallelization strategies, and applications are also reviewed.
GRASP: Basic components and enhancements
 Telecommunication Systems
, 2011
"... ..."
(Show Context)
EFFECTIVE APPLICATION OF GRASP
, 2009
"... A greedy randomized adaptive search procedure (GRASP) is an iterative multistart metaheuristic for difficult combinatorial optimization. Each GRASP iteration consists of two phases: a construction phase, in which a feasible solution is produced, and a local search phase, in which a local optimum in ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
A greedy randomized adaptive search procedure (GRASP) is an iterative multistart metaheuristic for difficult combinatorial optimization. Each GRASP iteration consists of two phases: a construction phase, in which a feasible solution is produced, and a local search phase, in which a local optimum in the neighborhood of the constructed solution is sought. Since 1989, GRASP has been applied to a wide range of combinatorial optimization problems, ranging from scheduling and routing to drawing and turbine balancing. In this paper, we cover the literature where GRASP is applied to scheduling,
ISTITUTO DI ANALISI DEI SISTEMI ED INFORMATICA “Antonio Ruberti” CONSIGLIO NAZIONALE DELLE RICERCHE
"... A Greedy Randomized Adaptive Search Procedure (GRASP) is an iterative multistart metaheuristic for difficult combinatorial optimization problems. Each GRASP iteration consists of two phases: a construction phase, in which a feasible solution is produced, and a local search phase, in which a local o ..."
Abstract
 Add to MetaCart
(Show Context)
A Greedy Randomized Adaptive Search Procedure (GRASP) is an iterative multistart metaheuristic for difficult combinatorial optimization problems. Each GRASP iteration consists of two phases: a construction phase, in which a feasible solution is produced, and a local search phase, in which a local optimum in the neighborhood of the constructed solution is sought. Repeated applications of the construction procedure yields different starting solutions for the local search and the best overall solution is kept as the result. The GRASP local search applies iterative improvement until a locally optimal solution is found. During this phase, starting from the current solution an improving neighbor solution is accepted and considered as new current solution. In this paper, we propose a variant of the GRASP framework that uses a new “nonmonotone” strategy to explore the neighborhood of the current solution. We formally state the convergence of the nonmonotone local search to a locally optimal solution and illustrate the effectiveness of the resulting Nonmonotone GRASP on the Maximum Cut Problem, a classical hard combinatorial optimization problem.
Integral Optimization of the Container Loading Problem
"... The rapid globalization of the world economy has led to the development of ample and quickly growing (aerial, maritime, terrestrial) networks for merchandise distribution in containers [Wang et al., 2008]. The transport costs afforded by the specialized companies operating in this sector are directl ..."
Abstract
 Add to MetaCart
(Show Context)
The rapid globalization of the world economy has led to the development of ample and quickly growing (aerial, maritime, terrestrial) networks for merchandise distribution in containers [Wang et al., 2008]. The transport costs afforded by the specialized companies operating in this sector are directly related to appropriate loading and efficient use of space
Departamento de Ciência de Computadores
"... Abstract In this paper we introduce a GRASP for the solution of general linear integer problems. The strategy is based on the separation of the set of variables into the integer subset and the continuous subset. The integer variables are fixed by GRASP and replaced in the original linear problem. I ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract In this paper we introduce a GRASP for the solution of general linear integer problems. The strategy is based on the separation of the set of variables into the integer subset and the continuous subset. The integer variables are fixed by GRASP and replaced in the original linear problem. If the original problem had continuous variables, it becomes a pure continuous problem, which can be solved by a linear program solver to determine the objective value corresponding to the fixed variables. If the original problem was a pure integer problem, simple algebraic manipulations can be used to determine the objective value that corresponds to the fixed variables. When we assign values to integer variables that lead to an impossible linear problem, the evaluation of the corresponding solution is given by the sum of infeasibilities, together with an infeasibility flag. We report results obtained for some standard benchmark problems, and compare them to those obtained by branchandbound and to those obtained by an evolutionary solver.