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检索条件"主题词=combinatorial optimization problems"

共 159 条记录，以下是101-110 订阅

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ACYCLIC ORIENTATIONS WITH PATH CONSTRAINTS

ACYCLIC ORIENTATIONS WITH PATH CONSTRAINTS

引用

5th ALIO/EURO Workshop on Applied combinatorial optimization

作者： Figueiredo, Rosa M. V. Barbosa, Valmir C. Maculan, Nelson de Souza, Cid C. Univ Estado Rio de Janeiro Inst Matemat & Estat BR-20550900 Rio De Janeiro Brazil Univ Estado Rio de Janeiro COPPE Programa Engn & Sistemas Comp BR-21941972 Rio De Janeiro Brazil Univ Estadual Campinas Inst Comp BR-13084971 Campinas SP Brazil

Many well-known combinatorial optimization problems can be stated over the set of acyclic orientations of an undirected graph. For example, acyclic orientations with certain diameter constraints are closely related to the optimal solutions of the vertex coloring and frequency assignment problems. In this paper we introduce a linear programming formulation of acyclic orientations with path constraints, and discuss its use in the solution of the vertex coloring problem and some versions of the frequency assignment problem. A study of the polytope associated with the formulation is presented, including proofs of which constraints of the formulation are facet-defining and the introduction of new classes of valid inequalities.

关键词： Acyclic orientations path constraints combinatorial optimization problems facets of polyhedra

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An Exploration Technique for the Interacted Multiple Ant Colonies optimization Framework

An Exploration Technique for the Interacted Multiple Ant Col...

引用

UKSim/AMSS 1st International Conference on Intelligent Systems, Modelling and Simulation

作者： Aljanaby, Alaa Ku-Mahamud, Ku Ruhana Norwawi, Norita Md Univ Utara Malaysia Coll Arts & Sci Sintok 06010 Kedah Malaysia Univ Sains Islam Malaysia Fac Sci & Technol Bandar Baru Nila 71800 Malaysia

ISBN: (纸本)9781424459841

Interacted Multiple Ant Colonies optimization (IMACO) is a newly proposed framework. In this framework several colonies of artificial ants are utilized. These colonies are working cooperatively to solve an optimization problem using some interaction technique. Exploration technique is doing an essential job in this framework. This technique is responsible for directing the activity of utilized colonies towards the different parts of the huge search space. This paper describes the newly proposed IMACO framework and proposes an effective exploration technique. Computational tests show that the new exploration technique can furthermore improve the IMACO performance. These tests also show the capability of IMACO to outperform other well known ant algorithms like ant colony system and max-min ant system.

关键词： ant colony optimization combinatorial optimization problems search stagnation exploration exploitation

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Towards an Answer Set Programming Methodology for Constructing Programs Following a Semi-Automatic Approach - Extended and Revised version 12

Towards an Answer Set Programming Methodology for Constructi...

引用

12th Latin American Workshop on Logic/Languages, Algorithms and New Methods of Reasoning (LANMR)

作者： Everardo, Flavio Osorio, Mauricio Univ Potsdam Potsdam Germany Univ Americas Puebla Cholula Mexico Tecnol Monterrey Campus Puebla Puebla Mexico

Answer Set Programming (ASP) is a successful rule-based formalism for modeling and solving knowledge-intense combinatorial (optimization) problems. Despite its success in both academic and industry, open challenges like automatic source code optimization, and software engineering remains. This is because a problem encoded into an ASP might not have the desired solving performance compared to an equivalent representation. Motivated by these two challenges, this paper has three main contributions. First, we propose a developing process towards a methodology to implement ASP programs, being faithful to existing methods. Second, we present ASP encodings that serve as the basis from the developing process. Third, we demonstrate the use of ASP to reverse the standard solving process. That is, knowing answer sets in advance, and desired strong equivalent properties, "we" exhaustively reconstruct ASP programs if they exist. This paper was originally motivated by the search of propositional formulas (if they exist) that represent the semantics of a new aggregate operator. Particularly, a parity aggregate. This aggregate comes as an improvement from the already existing parity (XOR) constraints from xorro, where lacks expressiveness, even though these constraints fit perfectly for reasoning modes like sampling or model counting. To this end, this extended version covers the fundaments from parity constraints as well as the xorro system. Hence, we delve a little more in the examples and the proposed methodology over parity constraints. Finally, we discuss our results by showing the only representation available, that satisfies different properties from the classical logic xor operator, which is also consistent with the semantics of parity constraints from xorro.

关键词： nswer set programming combinatorial optimization problems parity aggregate operator

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A Fuzzy Approach of Sensitivity for Multiple Colonies on Ant Colony optimization 7th

A Fuzzy Approach of Sensitivity for Multiple Colonies on Ant...

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7th International Workshop on Soft Computing Applications (SOFA)

作者： Pintea, Camelia-M. Matei, Oliviu Ramadan, Rabie A. Pavone, Mario Niazi, Muaz Azar, Ahmad Taher TU Cluj Napoca Cluj Napoca Romania Cairo Univ Egypt Giza Egypt Univ Catania Catania Italy COMSATS Pakistan Islamabad Pakistan Benha Univ Fac Comp & Informat Banha Egypt Nile Univ 6th Of October City Egypt

ISBN: (纸本)9783319625249

In order to solve combinatorial optimization problem are used mainly hybrid heuristics. Inspired from nature, both genetic and ant colony algorithms could be used in a hybrid model by using their benefits. The paper introduces a new model of Ant Colony optimization using multiple colonies with different level of sensitivity to the ant's pheromone. The colonies react different to the changing environment, based on their level of sensitivity and thus the exploration of the solution space is extended. Several discussion follows about the fuzziness degree of sensitivity and its influence on the solution of a complex problem.

关键词： Ant colony optimization Stigmergy Sensitivity Uncertainty Fuzzy data Traveling salesman problem combinatorial optimization problems

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Using an enhanced genetic algorithm to solve the unit commitment problem

Using an enhanced genetic algorithm to solve the unit commit...

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IEEE International Conference on Intelligent Processing Systems

作者： Zhu, MY Cen, WH Wang, MY Zhang, PC Shanghai Jiao Tong Univ Dept Elect Power Engn Shanghai 200240 Peoples R China

ISBN: (纸本)0780342534

Genetic algorithm (GA) is a general purpose optimization technique bused on mechanisms inspired from the natural genetics and natural selection. It is very suitable for solving nonlinear, multi-constraints, combinatorial optimization problems problems are tough for conventional methods. However, the simple genetic algorithm(SGA) may have a slow convergence or even cannot reach the global optimum. Therefore, an enhanced GA is proposed ill this paper to solve the unit commitment problem in power systems. The new features of the enhanced GA include chromosome mapping, problem specific operators and a local search technique. As expected, it has a significantly improved performance of finding the optimal solution to the unit commitment problem.

关键词： load distribution scheduling genetic algorithms power engineering computing problem solving convergence search problems enhanced genetic algorithm unit commitment general purpose optimization technique natural genetics natural selection nonlinear problem multiconstraint problem combinatorial optimization problems convergence global optimum power systems chromosome mapping problem specific operators local search technique performance

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A Trial Experiment of Deep Learning For Task Scheduling

A Trial Experiment of Deep Learning For Task Scheduling

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

作者： Kono, Jumpei Kai, Munenori Seikei Univ Grad Sch Sci & Technol Tokyo Japan

ISBN: (纸本)9781728127941

Task scheduling is one of the methods to minimize the processing time of a program in parallel processing. However, because task scheduling problems belong to strongly NP-hard combinatorial optimization problems [1], it is hard to find an optimal solution within a practical search time. In this research, we performed an experiment for speeding up the traditional search method based on the branch and bound algorithm by conducting scheduling with the use of deep learning.

关键词： Task Scheduling Deep Learning combinatorial optimization problems Branch and bound Convolutional Neural Network

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Toward an Automatic Approach to Greedy Algorithms

Toward an Automatic Approach to Greedy Algorithms

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3rd International Workshop on Frontiers in Algorithmics

作者： Zheng, Yujun Xue, Jinyun Zuo, Zhengkang Chinese Acad Sci Inst Software Beijing 100080 Peoples R China

ISBN: (纸本)9783642022692

The greedy approach is widely used for combinatorial optimization problems, but its implementation varies from problem to problem. In this paper we propose a mechanical approach for implementing greedy algorithmic programs. Using PAR, method, a problem can be continually partitioned into subproblems in smaller size based on the problem singleton and the maximum selector, and the greedy algorithm can be mechanically generated by combining the problem-solving sequences. Our structural model supports logical transformation from specifications to algorithmic programs by deductive inference;and thus significantly promotes the automation and reusability of algorithm design.

关键词： combinatorial optimization problems PAR method problem singleton greedy algorithm

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Magnifying computing gaps - Establishing encrypted communication over unidirectional channels (Extended abstract)

Magnifying computing gaps - Establishing encrypted communica...

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9th International Symposium on Stabilization, Safety and Security of Distributed Systems (SSS 2007)

作者： Dolev, Shlomi Korach, Ephraim Uzan, Galit Ben Gurion Univ Negev Dept Comp Sci IL-84105 Beer Sheva Israel Ben Gurion Univ Negev Dept Ind Engn IL-84105 Beer Sheva Israel

ISBN: (纸本)9783540766261

Consider two, not necessarily identical, powerful computers or computer-grids connected by a unidirectional communication link that should transfer a long stream of information in the presence of a listening adversary that is slightly weaker. We present schemes that enhance the computation strength gap between the powerful computers and the adversary. In other words, the gap between the amount of information decrypted by the adversary and the information decrypted by the receiver grows with time. We also suggest schemes based on the shortest vector problem in which only the receivers are computationally powerful. The scheme is self-stabilizing in the sense that it can establish a security level without relying on (previously distributed private keys that are part of) the state. The iterative nested approach suggested, can be used for enhancing the security of the classical protocol of Ralph Merkle [19]. Several applications for sensor networks and for secure communication with survivors are suggested.

关键词： unidirectional encryption combinatorial optimization problems

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RANDOM SEARCH IN HIGH DIMENSIONAL STOCHASTIC optimization

RANDOM SEARCH IN HIGH DIMENSIONAL STOCHASTIC OPTIMIZATION

引用

2010 Winter Simulation Conference

作者： Cheng, Russell Univ Southampton Southampton SO17 1BJ Hants England

ISBN: (纸本)9781424498642

We consider the use of random search for high dimensional optimization problems where the objective function to be optimized can only be computed with error. Random search is easy to carry out, but extraction of information concerning the objective function is not so straightforward. We propose fitting a statistical model to the objective function values obtained in such a search, and show how the fitted model can be used to estimate the best value obtained when the search effort is limited and how this value compares with the unknown true optimum value. A possible use of this approach is in combinatorial optimization problems. The dimension in such a problem is not usually considered, but if a dimension can be associated with it, then it is likely to be high. We illustrate our method with a numerical example involving a travelling salesman problem.

关键词： combinatorial optimization problems high dimensional stochastic optimization random search search problems statistical model stochastic processes travelling salesman problem travelling salesman problems unknown true optimum value

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A Variant of Quantum Genetic Algorithm and its Possible Applications

A Variant of Quantum Genetic Algorithm and its Possible Appl...

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International Conference on Soft Computing for Problem Solving (SocProS 2011)

作者： Tiwari, Pawan Kumar Vidyarthi, Deo Prakash Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

ISBN: (纸本)9788132204862

There are many problems solutions of which require a great deal of computation. Over the years, nature inspired computing has become a successful tool to solve such problems. Quantum Genetic Algorithm (QGA), in the recent, often being used and is gaining momentum. A variant of QGA is being proposed in this work, which consist of multiple chains and uses the concept of continuous basis for continuous domain problem.

关键词： combinatorial optimization problems Quantum computing Continuous basis Genetic Algorithms

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