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To solve the proposed model, a Benders decomposition algorithm enhanced by several accelerating methods is developed.
A newly evolved artificial bee colony (ABC) algorithm enhanced with opposition search has been used to perform the optimization task.
3d path planning using valid assembly sequence information was optimized by combining particle swarm optimization algorithm enhanced by the potential field path planning concepts.
The reduced stochastic large-scale optimization problem is then solved via a L-shaped algorithm enhanced with surrogate constraints and Pareto-optimal cuts.
A heuristic method is used to solve the model, based on a simulated annealing algorithm enhanced with a local improvement procedure.
The proposed method is a decentralized frontier based algorithm enhanced by a cost/utility evaluation function that considers the odor concentration and airflow at each frontier.
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Experimental results shows that our algorithm enhance the accuracy in diagnosis of heart disease.
Also, the ACLAF algorithm enhances the prediction gain by more than 25% when compared to the other augmented algorithms.
In this paper, an improved cuckoo search algorithm, enhancing the accuracy and convergence rate of the cuckoo search algorithm, is presented.
Testing on synthetic and real data sets at high levels of additive noise shows that the algorithm enhances the first-break picking considerably.
The algorithm enhances both LIPC and HIPC images.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com