AE1: An extension matrix approximate method for the general covering problem

The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity. It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

Download Full-text

A new approach using a Binary Firefly Algorithm for the Set Covering Problem

Electrical Engineering and Information Technology ◽

10.2495/ceeit140081 ◽

2014 ◽

Cited By ~ 3

Author(s):

Broderick Crawford ◽

Ricardo Soto ◽

Miguel Olivares-Suárez ◽

Fernando Paredes

Keyword(s):

Firefly Algorithm ◽

Set Covering ◽

Covering Problem ◽

Set Covering Problem ◽

New Approach

Download Full-text

Similarity problems of the theory of unsteady concentration boundary layer

Collection of Czechoslovak Chemical Communications ◽

10.1135/cccc19832751 ◽

1983 ◽

Vol 48 (10) ◽

pp. 2751-2766

Author(s):

Ondřej Wein ◽

N. D. Kovalevskaya

Keyword(s):

Boundary Layer ◽

Approximate Method ◽

Steady Flow ◽

Broad Class ◽

Concentration Boundary Layer ◽

Concentration Change ◽

Concentration Boundary ◽

Flow Problems

Using a new approximate method, transient course of the local and mean diffusion fluxes following a step concentration change on the wall has been obtained for a broad class of steady flow problems.

Download Full-text

Improvement of the Approximate Method for the Comparison Operation in the RNS

2020 International Conference Engineering and Telecommunication (En&T) ◽

10.1109/ent50437.2020.9431290 ◽

2020 ◽

Author(s):

Egor Shiryaev ◽

Elena Golimblevskaia ◽

Mikhail Babenko ◽

Andrei Tchernykh ◽

Bernardo Pulido-Gaytan

Keyword(s):

Approximate Method ◽

Comparison Operation

Download Full-text

Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

Mathematics ◽

10.3390/math9161839 ◽

2021 ◽

Vol 9 (16) ◽

pp. 1839

Author(s):

Broderick Crawford ◽

Ricardo Soto ◽

José Lemus-Romani ◽

Marcelo Becerra-Rozas ◽

José M. Lanza-Gutiérrez ◽

...

Keyword(s):

Combinatorial Problems ◽

Set Covering ◽

Covering Problem ◽

Integration Framework ◽

Q Learning ◽

Whale Optimization ◽

Sine Cosine Algorithm ◽

Exploration Exploitation ◽

Selection Of

One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.

Download Full-text

An Analysis of a KNN Perturbation Operator: An Application to the Binarization of Continuous Metaheuristics

Mathematics ◽

10.3390/math9030225 ◽

2021 ◽

Vol 9 (3) ◽

pp. 225

Author(s):

José García ◽

Gino Astorga ◽

Víctor Yepes

Keyword(s):

Transfer Functions ◽

Optimization Problems ◽

Optimization Methods ◽

Set Covering ◽

Covering Problem ◽

Perturbation Operator ◽

K Nearest Neighbors ◽

Combinatorial Optimization Problems ◽

Box Plots ◽

Metaheuristic Techniques

The optimization methods and, in particular, metaheuristics must be constantly improved to reduce execution times, improve the results, and thus be able to address broader instances. In particular, addressing combinatorial optimization problems is critical in the areas of operational research and engineering. In this work, a perturbation operator is proposed which uses the k-nearest neighbors technique, and this is studied with the aim of improving the diversification and intensification properties of metaheuristic algorithms in their binary version. Random operators are designed to study the contribution of the perturbation operator. To verify the proposal, large instances of the well-known set covering problem are studied. Box plots, convergence charts, and the Wilcoxon statistical test are used to determine the operator contribution. Furthermore, a comparison is made using metaheuristic techniques that use general binarization mechanisms such as transfer functions or db-scan as binarization methods. The results obtained indicate that the KNN perturbation operator improves significantly the results.

Download Full-text

A fast $$(2 + \frac{2}{7})$$-approximation algorithm for capacitated cycle covering

Mathematical Programming ◽

10.1007/s10107-021-01678-3 ◽

2021 ◽

Author(s):

Vera Traub ◽

Thorben Tröbst

Keyword(s):

Vehicle Routing Problem ◽

Covering Problem ◽

Post Processing ◽

Routing Problem ◽

Cycle Cover ◽

Disjoint Cycles ◽

Processing Step ◽

Number Of Cycles ◽

Vertex Disjoint ◽

Capacitated Vehicle

AbstractWe consider the capacitated cycle covering problem: given an undirected, complete graph G with metric edge lengths and demands on the vertices, we want to cover the vertices with vertex-disjoint cycles, each serving a demand of at most one. The objective is to minimize a linear combination of the total length and the number of cycles. This problem is closely related to the capacitated vehicle routing problem (CVRP) and other cycle cover problems such as min-max cycle cover and bounded cycle cover. We show that a greedy algorithm followed by a post-processing step yields a $$(2 + \frac{2}{7})$$ ( 2 + 2 7 ) -approximation for this problem by comparing the solution to a polymatroid relaxation. We also show that the analysis of our algorithm is tight and provide a $$2 + \epsilon $$ 2 + ϵ lower bound for the relaxation.

Download Full-text