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PARALLEL GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURES
, 2004
"... A GRASP (Greedy Randomized Adaptive Search Procedure) is a metaheuristic for producing goodquality solutions of combinatorial optimization problems. It is usually implemented with a construction procedure based on a greedy randomized algorithm followed by local search. In this Chapter, we survey p ..."
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A GRASP (Greedy Randomized Adaptive Search Procedure) is a metaheuristic for producing goodquality solutions of combinatorial optimization problems. It is usually implemented with a construction procedure based on a greedy randomized algorithm followed by local search. In this Chapter, we survey parallel implementations of GRASP. We describe simple strategies to implement independent parallel GRASP heuristics and more complex cooperative schemes using a pool of elite solutions to intensify the search process. Some applications of independent and cooperative parallelizations are presented in detail.
METAHEURISTIC HYBRIDIZATION WITH GRASP
"... 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 chapter we consider ways to hybridize GRASP to create new and more effective metaheuristics. We c ..."
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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 chapter we consider ways to hybridize GRASP to create new and more effective metaheuristics. We consider several types of hybridizations: constructive procedures, enhanced local search, memory structures, and cost reformulations.
3D Object Recognition and Pose Estimation using Feature Descriptor Regression in a Bayes’ Framework
, 2012
"... 1.1 Object recognition and pose estimation................... 5 1.2 3D Object Recognition and Pose Estimation................ 6 ..."
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1.1 Object recognition and pose estimation................... 5 1.2 3D Object Recognition and Pose Estimation................ 6
Improved heuristics for the regenerator location problem
, 2014
"... Telecommunication systems use optical signals to transmit information. The strength of a signal in an optical network deteriorates and loses power as it goes farther from the source, mainly due to attenuation. Therefore, to enable the signal to arrive its intended destination with good quality, it i ..."
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Telecommunication systems use optical signals to transmit information. The strength of a signal in an optical network deteriorates and loses power as it goes farther from the source, mainly due to attenuation. Therefore, to enable the signal to arrive its intended destination with good quality, it is necessary to regenerate the signal periodically using regenerators. These components are relatively expensive and therefore it is desirable to deploy as few of them as possible in the network. In the regenerator location problem (RLP), we are given an undirected graph, positive edge lengths, and a parameter specifying the maximum length that a signal can travel before its quality deteriorates and regeneration is required. The problem consists in determining paths that connect all pairs of nodes in the graph and, if necessary, locating single regenerators in some of those nodes such that the signal never travels more than the maximum allowed distance without traversing a regenerator node. In this paper, we present new implementations of previous heuristics and two new heuristics—aGRASP and a biased randomkey genetic algorithm—for the RLP. Computational experiments comparing the proposed solution procedures with previous heuristics described in the literature illustrate the efficiency and effectiveness of our methods.