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A Combinatorial Optimized knapsack Linear Space for Information Retrieval

Computers, Materials & Continua 2021年第3期66卷 2891-2903页

作者： Varghese S.Chooralil Vinodh P.Vijayan Biju Paul M.M.Anishin Raj B.Karthikeyan G.Manikandan Rajagiri School of Engineering&Technology Kochi682039India Mangalam College of Engineering Kottayam686631India Department of CSE Viswajyothi College of Engineering&TechnologyVazhakulam686670India School of Computing SASTRA Deemed To Be UniversityThanjavur613401India

Key information extraction can reduce the dimensional effects while evaluating the correct preferences of users during semantic data ***,the classifiers are used to maximize the performance of web-page recommendation in terms of precision and *** recent method disambiguates contextual sentiment using conceptual prediction with robustness,however the conceptual prediction method is not able to yield the optimal ***-dependent terms are primarily evaluated by constructing linear space of context features,presuming that if the terms come together in certain consumerrelated reviews,they are semantically ***,the more frequently they coexist,the greater the semantic dependency ***,the influence of the terms that coexist with each other can be part of the frequency of the terms of their semantic dependence,as they are non-integrative and their individual meaning cannot be *** this work,we consider the strength of a term and the influence of a term as a combinatorial optimization,called Combinatorial Optimized Linear Space knapsack for Information Retrieval(COLSK-IR).The COLSK-IR is considered as a knapsack problem with the total weight being the“term influence”or“influence of term”and the total value being the“term frequency”or“frequency of term”for semantic data *** method,by which the term influence and the term frequency are considered to identify the optimal solutions,is called combinatorial ***,we choose the knapsack for performing an integer programming problem and perform multiple experiments using the linear space through combinatorial optimization to identify the possible optimum *** is evident from our experimental results that the COLSK-IR provides better results than previous methods to detect strongly dependent snippets with minimum ambiguity that are related to inter-sentential context during semantic data analysis.

关键词： Key information extraction web-page context-dependent nonintegrative combinatorial optimization knapsack

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Approximation for multi-knapsack problem

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Chinese Science Bulletin 1996年第12期41卷 1042-1045页

作者：张立昂李路阳黄雄 Department of Computer Science and Technology Peking University Beijing 100871 China

Suppose that ∏ is a maximization problem,and tha A is an approximation algorithmfor ∏.For every instance I of ∏,define R_A(I)=OPT(I)/A(I),where OPT(I)is the optimal value of I;A(I)is the value of approximate soluti... 详细信息

关键词： knapsack problem approximation algorithm for combinatorial problems computational complexity.

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SENSITIVITY ANALYSIS OF THE knapsack PROBLEM:TIGHTER LOWER AND UPPER BOUND LIMITS

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Journal of Systems Science and Systems Engineering 2008年第4期17卷 156-170页

作者： Tarik BELGACEM Mhand HIFI CES-CNRS UMR 8174 Equipe CERMSEMUniversité Paris 1 Panthon-Sorbonne 106-112Boulevard de l’Hpital75647 Paris cedex 13France MIS Axis:Discrete Optimization and Re-optimizationUniversité de Picardie Jules Verne 33 rue Saint Leu80039 Amiens cedex 01France

In this paper, we study the sensitivity of the optimum of the knapsack problem to the perturbation of the profit of a subset of items. We propose a polynomial heuristic in order to establish both lower and upper bound limits of the sensitivity interval. The aim is to stabilize any given optimal solution obtained by applying any exact algorithm. We then evaluate the effectiveness of the proposed solution procedure on an example and a set of randomly generated problem instances.

关键词： Combinatorial optimization knapsack sensitivity analysis

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On the Structure of 0/1 Balance knapsack Module 2~N

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The Journal of China Universities of Posts and Telecommunications 2003年第1期10卷 34-38页

作者： ZHANGChun-qi YANGYi-xian YOULin InformationSecurityCenter BeijingUniveristyofPostandTelecommunicationsBeijing100876P.R.China DepartmentofAppliedMathematics DalianUniversityofTechnologyDalian116023P.R.China

In this paper, we prove that the 0/1 balance knapsack module 2~N isequivalent to the standard balance knapsack with its weight matrix being the upper triangle matrix,its number equals to 2^(N(N-1)/2)N(! ), and the ith... 详细信息

关键词： knapsack complexity structure

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Big Data Flow Adjustment Using knapsack Problem

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Journal of Computer and Communications 2018年第10期6卷 30-39页

作者： Eyman Yosef Ahmed Salama M. Elsayed Wahed Department of Mathematics Faculty of Sience Bour Said University Bour Said Egypt Faculty of Computers and Informatics Suez Canal University Ismailia Egypt

The advancements of mobile devices, public networks and the Internet of creature huge amounts of complex data, both construct & unstructured are being captured in trust to allow organizations to produce better business decisions as data is now pivotal for an organizations success. These enormous amounts of data are referred to as Big Data, which enables a competitive advantage over rivals when processed and analyzed appropriately. However Big Data Analytics has a few concerns including Management of Data, Privacy & Security, getting optimal path for transport data, and Data Representation. However, the structure of network does not completely match transportation demand, i.e., there still exist a few bottlenecks in the network. This paper presents a new approach to get the optimal path of valuable data movement through a given network based on the knapsack problem. This paper will give value for each piece of data, it depends on the importance of this data (each piece of data defined by two arguments size and value), and the approach tries to find the optimal path from source to destination, a mathematical models are developed to adjust data flows between their shortest paths based on the 0 - 1 knapsack problem. We also take out computational experience using the commercial software Gurobi and a greedy algorithm (GA), respectively. The outcome indicates that the suggest models are active and workable. This paper introduced two different algorithms to study the shortest path problems: the first algorithm studies the shortest path problems when stochastic activates and activities does not depend on weights. The second algorithm studies the shortest path problems depends on weights.

关键词： 0 - 1 knapsack Problem Big Data Big Data Analytics Big Dao Ta Inconsistencies

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Multi-knapsack Model of Collaborative Portfolio Configurations in Multi-Strategy Oriented

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American Journal of Operations Research 2015年第5期5卷 401-408页

作者： Shujuan Luo Sijun Bai Suike Li School of Management Northwestern Polytechnical University Xi’an China

Aiming at constructing the multi-knapsack model of collaborative portfolio configurations in multi-strategy oriented, the hybrid evolutionary algorithm was designed based on greedy method, combining with the organization of the multiple strategical guidance and multi-knapsack model. Furthermore, the organizing resource utility and risk management of portfolio were considered. The experiments were conducted on three main technological markets which contain communication, transportation and industry. The results demonstrated that the proposed model and algorithm were feasible and reliable.

关键词： Multi knapsack Model Multi Strategy Collaborative Portfolio Evolutionary Algorithm

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An Improved Binary Wolf Pack Algorithm Based on Adaptive Step Length and Improved Update Strategy for 0-1 knapsack Problems

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国际计算机前沿大会会议论文集 2017年第2期 105-106页

作者： Liting Guo Sanyang Liu School of Mathematics and Statistics Xidian UniversityXi’an 710126 Shaanxi China

Binary wolf pack algorithm (BWPA) is a kind of intelligence algorithm which can solve combination optimization problems in discrete *** on BWPA, an improved binary wolf pack algorithm (AIBWPA) can be proposed by adopting adaptive step length and improved update strategy of wolf pack. AIBWPA is applied to 10 classic 0-1 knapsack problems and compared with BWPA, DPSO, which proves that AIBWPA has higher optimization accuracy and better computational robustness. AIBWPA makes the parameters simple, protects the population diversity and enhances the global convergence.

关键词： Binary wolf pack algorithm 0-1 knapsack problem Adaptive step length Update strategy

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On Merging Cover Inequalities for Multiple knapsack Problems

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Open Journal of Optimization 2015年第4期4卷 141-155页

作者： Randal Hickman Todd Easton Department of Mathematical Sciences United States Military Academy West Point USA Industrial and Manufacturing Systems Engineering Department Kansas State University Manhattan USA

This paper describes methods to merge two cover inequalities and also simultaneously merge multiple cover inequalities in a multiple knapsack instance. Theoretical results provide conditions under which merged cover inequalities are valid. Polynomial time algorithms are created to find merged cover inequalities. A computational study demonstrates that merged inequalities improve the solution times for benchmark multiple knapsack instances by about 9% on average over CPLEX with default settings.

关键词： Multiple knapsack Problem Cutting Plane Cover Inequality Inequality Merging Pseudocost Integer Programming

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The 0/1 Multidimensional knapsack Problem and Its Variants: A Survey of Practical Models and Heuristic Approaches

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American Journal of Operations Research 2018年第5期8卷 395-439页

作者： Soukaina Laabadi Mohamed Naimi Hassan El Amri Boujemaa Achchab ENS-Hassan II University Laboratory of Mathematics and Applications Casablanca Morocco EST-Hassan I University Laboratory of Analysis Modeling Systems and Decision Support Berrechid Morocco

The 0/1 Multidimensional knapsack Problem (0/1 MKP) is an interesting NP-hard combinatorial optimization problem that can model a number of challenging applications in logistics, finance, telecommunications and other fields. In the 0/1 MKP, a set of items is given, each with a size and value, which has to be placed into a knapsack that has a certain number of dimensions having each a limited capacity. The goal is to find a subset of items leading to the maximum total profit while respecting the capacity constraints. Even though the 0/1 MKP is well studied in the literature, we can just find a little number of recent review papers on this problem. Furthermore, the existing reviews focus particularly on some specific issues. This paper aims to give a general and comprehensive survey of the considered problem so that it can be useful for both researchers and practitioners. Indeed, we first describe the 0/1 MKP and its relevant variants. Then, we present the detailed models of some important real-world applications of this problem. Moreover, an important collection of recently published heuristics and metaheuristics is categorized and briefly reviewed. These approaches are then quantitatively compared through some indicative statistics. Finally, some synthetic remarks and research directions are highlighted in the conclusion.

关键词： 0/1 Multidimensional knapsack Problem Survey Real-World Applications Heuristics Metaheuristics

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A Class of Continuous Separable Nonlinear Multidimensional knapsack Problems

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American Journal of Operations Research 2018年第4期8卷 266-280页

作者： Bin Zhang Zhe Lin Yu Wang Lingnan College Sun Yat-sen University Guangzhou China School of Economic and Business Administration Chongqing University Chongqing China

The nonlinear multidimensional knapsack problem is defined as the minimization of a convex function with multiple linear constraints. The methods developed for nonlinear multidimensional programming problems are often applied to solve the nonlinear multidimensional knapsack problems, but they are inefficient or limited since most of them do not exploit the characteristics of the knapsack problems. In this paper, by establishing structural properties of the continuous separable nonlinear multidimensional knapsack problem, we develop a multi-tier binary solution method for solving the continuous nonlinear multidimensional knapsack problems with general structure. The computational complexity is polynomial in the number of variables. We presented two examples to illustrate the general application of our method and we used statistical results to show the effectiveness of our method.

关键词： Nonlinear Programming Convex Programming Multidimensional knapsack Separable knapsack Lagrangian Relaxation

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