scholarly journals FINDING OF OPTIMAL SUBSET STRUCTURE IN THE KNAPSACK PROBLEM

Doklady BGUIR ◽  
2019 ◽  
pp. 72-79
Author(s):  
S. V. Chebakov ◽  
L. V. Serebryanaya
Keyword(s):  
Initial Data ◽  
Knapsack Problem ◽  
Combinatorial Problem ◽  
Pareto Set ◽  
Knapsack Problems ◽  
Data Set ◽  
Optimal Subset ◽  
Specific Subset ◽  
Large Numbers

An algorithm for solving the knapsack problem based on the proposed multi-criteria model is considered. The implementation of this algorithm allows to define the structure of the optimal subset as a union of certain elements of a Pareto layers group into which a initial data set is divided. The first such layer is the Pareto set. The optimal subset allows to find a specific subset of the initial data. Its elements as a result of belonging to the Pareto layers with large numbers cannot enter the optimal subset. The most expensive in terms of the number of operations required are knapsack problems, in which the number of elements in the set of initial data is quite large. The article shows that with a relatively small value of the knapsack volume, the number of elements required to find the optimal subset is significantly less than their total number in the original set. It can lead to a significant decrease in the total time to solve the combinatorial problem.  

scholarly journals Finding algorithm of optimal subset structure based on the Pareto layers in the knapsack problem

2020 ◽  
pp. 97-104
Author(s):  
Sergey V. Chebakov ◽  
Liya V. Serebryanaya
Keyword(s):  
Initial Data ◽  
Knapsack Problem ◽  
Optimization Model ◽  
Multicriteria Optimization ◽  
Combinatorial Problem ◽  
Optimal Subset ◽  
Preference Space

An algorithm is developed for finding the structure of the optimal subset in the knapsack problem based on the proposed multicriteria optimization model. A two-criteria relation of preference between elements of the set of initial data is introduced. This set has been split into separate Pareto layers. The depth concept of the elements dominance of an individual Pareto layer is formulated. Based on it, conditions are determined under which the solution to the knapsack problem includes the first Pareto layers. They are defined on a given set of initial data. The structure of the optimal subset is presented, which includes individual Pareto layers. Pareto layers are built in the introduced preference space. This does not require algorithms for enumerating the elements of the initial set. Such algorithms are used when finding only some part of the optimal subset. This reduces the number of operations required to solve the considered combinatorial problem.  The method for determining the found Pareto layers shows that the number of operations depends on the volume of the knapsack and the structure of the Pareto layers, into which the set of initial data in the entered two-criteria space is divided.

scholarly journals Optimization Method to Address Psychosocial Risks through Adaptation of the Multidimensional Knapsack Problem

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1126
Author(s):  
Marta Lilia Eraña-Díaz ◽  
Marco Antonio Cruz-Chávez ◽  
Fredy Juárez-Pérez ◽  
Juana Enriquez-Urbano ◽  
Rafael Rivera-López ◽  
...  
Keyword(s):  
Risk Factors ◽  
Simulated Annealing ◽  
Knapsack Problem ◽  
Simulated Annealing Algorithm ◽  
Psychosocial Risk Factors ◽  
Psychosocial Risk ◽  
Multidimensional Knapsack Problem ◽  
Optimal Subset ◽  
Multidimensional Knapsack ◽  
A Company

This paper presents a methodological scheme to obtain the maximum benefit in occupational health by attending to psychosocial risk factors in a company. This scheme is based on selecting an optimal subset of psychosocial risk factors, considering the departments’ budget in a company as problem constraints. This methodology can be summarized in three steps: First, psychosocial risk factors in the company are identified and weighted, applying several instruments recommended by business regulations. Next, a mathematical model is built using the identified psychosocial risk factors information and the company budget for risk factors attention. This model represents the psychosocial risk optimization problem as a Multidimensional Knapsack Problem (MKP). Finally, since Multidimensional Knapsack Problem is NP-hard, one simulated annealing algorithm is applied to find a near-optimal subset of factors maximizing the psychosocial risk care level. This subset is according to the budgets assigned for each of the company’s departments. The proposed methodology is detailed using a case of study, and thirty instances of the Multidimensional Knapsack Problem are tested, and the results are interpreted under psychosocial risk problems to evaluate the simulated annealing algorithm’s performance (efficiency and efficacy) in solving these optimization problems. This evaluation shows that the proposed methodology can be used for the attention of psychosocial risk factors in real companies’ cases.

scholarly journals Automatic Benchmark Generation Framework for Malware Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Guanghui Liang ◽  
Jianmin Pang ◽  
Zheng Shan ◽  
Runqing Yang ◽  
Yihang Chen
Keyword(s):  
Initial Data ◽  
Malware Detection ◽  
Detection Methods ◽  
Small Data ◽  
Security Threats ◽  
Improved Genetic Algorithm ◽  
Data Set ◽  
Detection Model ◽  
Manual Selection ◽  
Selection Of

To address emerging security threats, various malware detection methods have been proposed every year. Therefore, a small but representative set of malware samples are usually needed for detection model, especially for machine-learning-based malware detection models. However, current manual selection of representative samples from large unknown file collection is labor intensive and not scalable. In this paper, we firstly propose a framework that can automatically generate a small data set for malware detection. With this framework, we extract behavior features from a large initial data set and then use a hierarchical clustering technique to identify different types of malware. An improved genetic algorithm based on roulette wheel sampling is implemented to generate final test data set. The final data set is only one-eighteenth the volume of the initial data set, and evaluations show that the data set selected by the proposed framework is much smaller than the original one but does not lose nearly any semantics.

Feature Selection and Knapsack Problem Resolution Based on a Discrete Backtracking Optimization Algorithm

2021 ◽  
Vol 12 (2) ◽  
pp. 1-15
Author(s):  
Khadoudja Ghanem ◽  
Abdesslem Layeb
Keyword(s):  
Feature Selection ◽  
Knapsack Problem ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Knapsack Problems ◽  
Support Vector ◽  
Discrete Version ◽  
Search Optimization ◽  
Discrete Algorithms

Backtracking search optimization algorithm is a recent stochastic-based global search algorithm for solving real-valued numerical optimization problems. In this paper, a binary version of backtracking algorithm is proposed to deal with 0-1 optimization problems such as feature selection and knapsack problems. Feature selection is the process of selecting a subset of relevant features for use in model construction. Irrelevant features can negatively impact model performances. On the other hand, knapsack problem is a well-known optimization problem used to assess discrete algorithms. The objective of this research is to evaluate the discrete version of backtracking algorithm on the two mentioned problems and compare obtained results with other binary optimization algorithms using four usual classifiers: logistic regression, decision tree, random forest, and support vector machine. Empirical study on biological microarray data and experiments on 0-1 knapsack problems show the effectiveness of the binary algorithm and its ability to achieve good quality solutions for both problems.

Improving Quality and Safety Through Use of Secondary Data: Methods Case Study

2016 ◽  
Vol 39 (11) ◽  
pp. 1477-1501 ◽  
Author(s):  
Victoria Goode ◽  
Nancy Crego ◽  
Michael P. Cary ◽  
Deirdre Thornlow ◽  
Elizabeth Merwin
Keyword(s):  
Research Question ◽  
Secondary Data ◽  
Data Access ◽  
Data Sets ◽  
Complex Data ◽  
Management Skills ◽  
Data Set ◽  
Large Numbers ◽  
Need To Evaluate

Researchers need to evaluate the strengths and weaknesses of data sets to choose a secondary data set to use for a health care study. This research method review informs the reader of the major issues necessary for investigators to consider while incorporating secondary data into their repertoire of potential research designs and shows the range of approaches the investigators may take to answer nursing research questions in a variety of context areas. The researcher requires expertise in locating and judging data sets and in the development of complex data management skills for managing large numbers of records. There are important considerations such as firm knowledge of the research question supported by the conceptual framework and the selection of appropriate databases, which guide the researcher in delineating the unit of analysis. Other more complex issues for researchers to consider when conducting secondary data research methods include data access, management and security, and complex variable construction.

scholarly journals Optimalisasi Penyelesaian Knapsack Problem Dengan Algoritma Genetika

2016 ◽  
pp. 182
Author(s):  
I Wayan Supriana
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Everyday Life ◽  
Knapsack Problem ◽  
Selection Process ◽  
Optimization Algorithms ◽  
Evolutionary Process ◽  
Knapsack Problems ◽  
Theory Of Evolution ◽  
Selection Of

Knapsack problems is a problem that often we encounter in everyday life. Knapsack problem itself is a problem where a person faced with the problems of optimization on the selection of objects that can be inserted into the container which has limited space or capacity. Problems knapsack problem can be solved by various optimization algorithms, one of which uses a genetic algorithm. Genetic algorithms in solving problems mimicking the theory of evolution of living creatures. The components of the genetic algorithm is composed of a population consisting of a collection of individuals who are candidates for the solution of problems knapsack. The process of evolution goes dimulasi of the selection process, crossovers and mutations in each individual in order to obtain a new population. The evolutionary process will be repeated until it meets the criteria o f an optimum of the resulting solution. The problems highlighted in this research is how to resolve the problem by applying a genetic algorithm knapsack. The results obtained by the testing of the system is built, that the knapsack problem can optimize the placement of goods in containers or capacity available. Optimizing the knapsack problem can be maximized with the appropriate input parameters.

scholarly journals Searching for binary central stars of planetary nebulae with Kepler

2011 ◽  
Vol 7 (S283) ◽  
pp. 344-345
Author(s):  
Dimitri Douchin ◽  
George H. Jacoby ◽  
Orsola De Marco ◽  
Steve B. Howell ◽  
Mattias Kronberger
Keyword(s):  
Initial Data ◽  
Light Curve ◽  
Planetary Nebulae ◽  
Central Star ◽  
Light Curves ◽  
Data Set ◽  
Lunar Cycles ◽  
Near Future ◽  
Central Stars

AbstractThe Kepler Observatory offers unprecedented photometric precision (<1 mmag) and cadence for monitoring the central stars of planetary nebulae, allowing the detection of tiny periodic light curve variations, a possible signature of binarity. With this precision free from the observational gaps dictated by weather and lunar cycles, we are able to detect companions at much larger separations and with much smaller radii than ever before. We have been awarded observing time to obtain light-curves of the central stars of the six confirmed and possible planetary nebulae in the Kepler field, including the newly discovered object Kn 61, at cadences of both 30 min and 1 min. Of these six objects, we could confirm for three a periodic variability consistent with binarity. Two others are variables, but the initial data set presents only weak periodicities. For the central star of Kn 61, Kepler data will be available in the near future.

scholarly journals A DYNAMICAL SYMMETRY OF THE QUASI-SPHERICAL (OR SPHERICAL) COLLAPSE

2005 ◽  
Vol 14 (10) ◽  
pp. 1761-1767 ◽  
Author(s):  
UJJAL DEBNATH ◽  
SUBENOY CHAKRABORTY ◽  
NARESH DADHICH
Keyword(s):  
Kinetic Energy ◽  
Initial Data ◽  
Dynamical Symmetry ◽  
Matter Field ◽  
Physical Parameters ◽  
Data Sets ◽  
Type I ◽  
Data Set ◽  
Spherical Collapse ◽  
Scaling Relationship

By linearly scaling the initial data set (mass and kinetic energy functions), it is found that the dynamics of quasi-spherical (or spherical) collapse remains invariant for dust or a general (Type I) matter field, provided the comoving radius is also appropriately scaled. This defines a symmetry of the quasi spherical (or spherical) collapse. That is, the linear transformation identifies an equivalence class of data sets which lead to the same end result as well as its evolution all through. In particular, it is shown that the physical parameters, density and shear remain invariant. What the transformation is exhibiting is an interesting scaling relationship between mass, kinetic energy and the size of the collapsing sphere which is respected not only by the initial data set but remarkably also by the dynamics of collapse.

scholarly journals Flexible Wolf Pack Algorithm for Dynamic Multidimensional Knapsack Problems

Research ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Husheng Wu ◽  
Renbin Xiao
Keyword(s):  
Dynamic Optimization ◽  
Optimization Problems ◽  
Real Life ◽  
Dynamic Environment ◽  
Combinatorial Problem ◽  
Knapsack Problems ◽  
Dynamic Optimization Problems ◽  
Practical Applications ◽  
Combinatorial Optimization Problems ◽  
Multidimensional Knapsack

Optimization problems especially in a dynamic environment is a hot research area that has attracted notable attention in the past decades. It is clear from the dynamic optimization literatures that most of the efforts have been devoted to continuous dynamic optimization problems although the majority of the real-life problems are combinatorial. Moreover, many algorithms shown to be successful in stationary combinatorial optimization problems commonly have mediocre performance in a dynamic environment. In this study, based on binary wolf pack algorithm (BWPA), combining with flexible population updating strategy, a flexible binary wolf pack algorithm (FWPA) is proposed. Then, FWPA is used to solve a set of static multidimensional knapsack benchmarks and several dynamic multidimensional knapsack problems, which have numerous practical applications. To the best of our knowledge, this paper constitutes the first study on the performance of WPA on a dynamic combinatorial problem. By comparing two state-of-the-art algorithms with the basic BWPA, the simulation experimental results demonstrate that FWPA can be considered as a feasibility and competitive algorithm for dynamic optimization problems.

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