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This paper proposes an intelligent and optimized scheme to solve parking space problem for a small city (e.g., Mauritius) using a reactive search technique (named as Tabu Search) assisted by rough set.
In a reactive search, you look at every job advert you possibly can, pick out opportunities that you think you might enjoy and that you have some chance of being offered and you fire off your applications.
Finally, a pure reactive search will only happen if the source and the destination are both mobile mesh nodes.
Our experiments on SAT instances show that reactive search strategies improve the performance of the solving algorithm.
We introduce new high level reactive search strategies based on a generic algorithm's controller that is able to schedule the basic variation operators of the evolutionary algorithm, according to the observed state of the search.
Whereas the first method, which is the focus of this article, is a reactive search technique that decides where to search next only based on local information about the objects in the robot's vicinity, the second algorithm is a more global and inference-based approach that explicitly reasons about the location of the target object given all observations made so far.
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In [2], comparative studies for four modern heuristic algorithms (reactive tabu search, tabu search, parallel simulated annealing, and genetic algorithms) are presented for the service restoration.
A reactive tabu search metaheuristic and a tabu search with an advanced recovery feature, together with a set covering procedure are used for solution improvement.
Reactive protocols only search for a path between two nodes when there are data to send.
Then, the reactive routing protocol searches for a valid entry in its forwarding tables.
We carried out systematic reviews and meta-analyses of the effects (relative risks) on prognosis of estimated glomerular filtration rate and highly sensitive C reactive protein by searching Medline and Embase up to November 2008.
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