GRASP: Greedy Randomized Adaptive Search Procedures
نویسندگان
چکیده
منابع مشابه
Grasp: Greedy Randomized Adaptive Search Procedures
GRASP, or greedy randomized adaptive search procedure, is a multi-start metaheuristic that repeatedly applies local search starting from solutions constructed by a randomized greedy algorithm. In this chapter 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. Combinatorial opti...
متن کاملGreedy Randomized Adaptive Search Procedures (grasp)
This paper is a survey of greedy randomized adaptive search procedures (GRASP). GRASP is a multi-start or iterative procedure where each GRASP iteration consists of a construction phase, where a feasible solution is constructed, followed by a local search procedure that finds a locally optimal solution. The construction phase of GRASP is essentially a randomized greedy algorithm. Repeated appli...
متن کاملGreedy Randomized Adaptive Search Procedures
GRASP (greedy randomized adaptive search procedure) is a metaheuristic for combinatorial optimization. GRASP usually is implemented as a multistart procedure, where each iteration is made up of a construction phase, where a randomized greedy solution is constructed, and a local search phase which starts at the constructed solution and applies iterative improvement until a locally optimal soluti...
متن کاملGreedy Randomized Adaptive Search Procedures
GRASP is an iterative multi-start metaheuristic for solving difficult combinatorial problems. Each GRASP iteration consists of two phases: a greedy adaptive randomized construction phase and a local search phase. Starting from the feasible solution built during the greedy adaptive randomized construction phase, the local search explores its neighborhood until a local optimum is found. The best ...
متن کاملGreedy Randomized Adaptive Search Procedures
GRASP is a multi-start 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 c...
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ژورنال
عنوان ژورنال: INTELIGENCIA ARTIFICIAL
سال: 2003
ISSN: 1988-3064,1137-3601
DOI: 10.4114/ia.v7i19.716