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The metaheuristics Ant Colony Optimization (ACO) algorithm was combined with a gradient based algorithm to obtain a highly effective hybrid optimization algorithm for neural network training.
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They designed a computer-based algorithm to obtain the corresponding LIC automatically and have made it available free of charge online (from: http://oernst.f51vg.free.fr/liver/iron.html).html
The final prediction stage applies rule based algorithm to the values obtained from the initial stage.
In this paper we present a cycle detection based greedy algorithm, to obtain a minimal spanning tree of a given input weighted undirected graph.
In the case analysis, a sensitivity analysis method is adopted to obtain the minimum budget, and probabilistic planning based on genetic algorithm to obtain a satisfactory solution that is closer to reality.
In this paper, to further improve the quality of the VN embedding solution, we devise an ABC based VN embedding algorithm to obtain near-optimal solutions in terms of the ratio of the long-term average revenue and the VN request acceptance.
The proposed algorithm implements several valid inequalities adapted from the literature for the problem and a local search based on simulated annealing algorithm to obtain upper bounds.
We then develop an efficient network coding based cache management (NCCM) algorithm to obtain a near-optimal caching and routing solution for ICNs.
Since test prioritization can provide a way to simulate the more important tests which can improve the coverage metrics evidently earlier, we propose a test prioritization approach based on the clustering algorithm to obtain a high coverage level earlier in the simulation process.
Then based on this result a complementary algorithm to obtain a stabilizing stable reduced controller based on the two sided frequency weighted reduction method is presented.
Since the virtual access network embedding problem is NP-hard, a heuristic algorithm is developed based on an enhanced genetic algorithm to obtain an approximate but effective solution.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com