Local Search In Combinatorial Optimization

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Local Search In Combinatorial Optimization

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Author : Emile Aarts
language : en
Publisher: Princeton University Press
Release Date : 2018-06-05

Local Search In Combinatorial Optimization written by Emile Aarts and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-05 with Mathematics categories.

In the past three decades, local search has grown from a simple heuristic idea into a mature field of research in combinatorial optimization that is attracting ever-increasing attention. Local search is still the method of choice for NP-hard problems as it provides a robust approach for obtaining high-quality solutions to problems of a realistic size in reasonable time. Local Search in Combinatorial Optimization covers local search and its variants from both a theoretical and practical point of view, each topic discussed by a leading authority. This book is an important reference and invaluable source of inspiration for students and researchers in discrete mathematics, computer science, operations research, industrial engineering, and management science. In addition to the editors, the contributors are Mihalis Yannakakis, Craig A. Tovey, Jan H. M. Korst, Peter J. M. van Laarhoven, Alain Hertz, Eric Taillard, Dominique de Werra, Heinz Mühlenbein, Carsten Peterson, Bo Söderberg, David S. Johnson, Lyle A. McGeoch, Michel Gendreau, Gilbert Laporte, Jean-Yves Potvin, Gerard A. P. Kindervater, Martin W. P. Savelsbergh, Edward J. Anderson, Celia A. Glass, Chris N. Potts, C. L. Liu, Peichen Pan, Iiro Honkala, and Patric R. J. Östergård.

Local Search In Combinatorial Optimization

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Author : Emile Aarts
language : en
Publisher:
Release Date : 2007-01-01

Local Search In Combinatorial Optimization written by Emile Aarts and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-01-01 with categories.

Theoretical Aspects Of Local Search

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Author : Wil Michiels
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-01-17

Theoretical Aspects Of Local Search written by Wil Michiels and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-01-17 with Mathematics categories.

Local search has been applied successfully to a diverse collection of optimization problems. However, results are scattered throughout the literature. This is the first book that presents a large collection of theoretical results in a consistent manner. It provides the reader with a coherent overview of the achievements obtained so far, and serves as a source of inspiration for the development of novel results in the challenging field of local search.

Constraint Based Local Search

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Author : Pascal Van Hentenryck
language : en
Publisher: MIT Press (MA)
Release Date : 2005

Constraint Based Local Search written by Pascal Van Hentenryck and has been published by MIT Press (MA) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.

The ubiquity of combinatorial optimization problems in our society is illustrated by the novel application areas for optimization technology, which range from supply chain management to sports tournament scheduling. Over the last two decades, constraint programming has emerged as a fundamental methodology to solve a variety of combinatorial problems, and rich constraint programming languages have been developed for expressing and combining constraints and specifying search procedures at a high level of abstraction. Local search approaches to combinatorial optimization are able to isolate optimal or near-optimal solutions within reasonable time constraints. This book introduces a method for solving combinatorial optimization problems that combines constraint programming and local search, using constraints to describe and control local search, and a programming language, COMET, that supports both modeling and search abstractions in the spirit of constraint programming. After an overview of local search including neighborhoods, heuristics, and metaheuristics, the book presents the architecture and modeling and search components of constraint-based local search and describes how constraint-based local search is supported in COMET. The book describes a variety of applications, arranged by meta-heuristics. It presents scheduling applications, along with the background necessary to understand these challenging problems. The book also includes a number of satisfiability problems, illustrating the ability of constraint-based local search approaches to cope with both satisfiability and optimization problems in a uniform fashion.

Stochastic Local Search Algorithms For Multiobjective Combinatorial Optimization

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Author : Luis F. Paquete
language : de
Publisher: IOS Press
Release Date : 2006

Stochastic Local Search Algorithms For Multiobjective Combinatorial Optimization written by Luis F. Paquete and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Business & Economics categories.

Stochastic Local Search algorithms were shown to give state-of-the-art results for many other problems, but little is known on how to design and analyse them for Multiobjective Combinatorial Optimization Problems. This book aims to fill this gap. It defines two search models that correspond to two distinct ways of tackling MCOPs by SLS algorithms."

Approximate Local Search In Combinatorial Optimization

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Author : James B. Orlin
language : en
Publisher:
Release Date : 2007

Approximate Local Search In Combinatorial Optimization written by James B. Orlin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.

Local search algorithms for combinatorial optimization problems are generally of pseudopolynomial running time, and polynomial-time algorithms are not often known for finding locally optimal solutions for NP-hard optimization problems. We introduce the concept of amp;ε-local optimality and show that, for every amp;ε gt; 0, an amp;ε-local optimum can be identified in time polynomial in the problem size and 1/amp;ε whenever the corresponding neighborhood can be searched in polynomial time. If the neighborhood can be searched in polynomial time for a amp;δ-local optimum, a variation of our main algorithm produces a (amp;δ + amp;ε)-local optimum in time polynomial in the problem size and 1/amp;ε. As a consequence, a combinatorial optimization problem has a fully polynomial-time approximation scheme if and only if the problem of determining a better neighbor in an exact neighborhood has a fully polynomial-time approximation scheme.

Oblivious And Non Oblivious Local Search For Combinatorial Optimization

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Author : Justin Ward
language : en
Publisher:
Release Date : 2012

Oblivious And Non Oblivious Local Search For Combinatorial Optimization written by Justin Ward and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.

Mathematical Programming Solver Based On Local Search

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Author : Frédéric Gardi
language : en
Publisher: John Wiley & Sons
Release Date : 2014-07-09

Mathematical Programming Solver Based On Local Search written by Frédéric Gardi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-09 with Computers categories.

This book covers local search for combinatorial optimization and its extension to mixed-variable optimization. Although not yet understood from the theoretical point of view, local search is the paradigm of choice for tackling large-scale real-life optimization problems. Today's end-users demand interactivity with decision support systems. For optimization software, this means obtaining good-quality solutions quickly. Fast iterative improvement methods, like local search, are suited to satisfying such needs. Here the authors show local search in a new light, in particular presenting a new kind of mathematical programming solver, namely LocalSolver, based on neighborhood search. First, an iconoclast methodology is presented to design and engineer local search algorithms. The authors' concern regarding industrializing local search approaches is of particular interest for practitioners. This methodology is applied to solve two industrial problems with high economic stakes. Software based on local search induces extra costs in development and maintenance in comparison with the direct use of mixed-integer linear programming solvers. The authors then move on to present the LocalSolver project whose goal is to offer the power of local search through a model-and-run solver for large-scale 0-1 nonlinear programming. They conclude by presenting their ongoing and future work on LocalSolver toward a full mathematical programming solver based on local search.

Approximate Local Search In Combinatorial Optimization

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Author : James B. Orlin
language : en
Publisher:
Release Date : 2004

Approximate Local Search In Combinatorial Optimization written by James B. Orlin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.

Local search algorithms for combinatorial optimization problems are in general of pseudopolynomial running time and polynomial-time algorithms are often not known for finding locally optimal solutions for NP-hard optimization problems. We introduce the concept of epsilon-local optimality and show that an epsilon-local optimum can be identified in time polynomial in the problem size and 1/epsilon whenever the corresponding neighborhood can be searched in polynomial time, for epsilon> 0. If the neighborhood can be searched in polynomial time for a delta-local optimum, we present an algorithm that produces a (delta+epsilon)-local optimum in time polynomial in the problem size and 1/epsilon. As a consequence, a combinatorial optimization problem has a fully polynomial-time approximation scheme if and only if it has a fully polynomial-time augmentation scheme. Keywords: Local Search, Neighborhood Search, Approximation Algorithms, Computational Complexity, Combinatorial Optimization, 0/1-Integer Programming.

Meta Heuristics

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Author : Stefan Voß
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Meta Heuristics written by Stefan Voß and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Business & Economics categories.

Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimizations comprises a carefully refereed selection of extended versions of the best papers presented at the Second Meta-Heuristics Conference (MIC 97). The selected articles describe the most recent developments in theory and applications of meta-heuristics, heuristics for specific problems, and comparative case studies. The book is divided into six parts, grouped mainly by the techniques considered. The extensive first part with twelve papers covers tabu search and its application to a great variety of well-known combinatorial optimization problems (including the resource-constrained project scheduling problem and vehicle routing problems). In the second part we find one paper where tabu search and simulated annealing are investigated comparatively and two papers which consider hybrid methods combining tabu search with genetic algorithms. The third part has four papers on genetic and evolutionary algorithms. Part four arrives at a new paradigm within meta-heuristics. The fifth part studies the behavior of parallel local search algorithms mainly from a tabu search perspective. The final part examines a great variety of additional meta-heuristics topics, including neural networks and variable neighbourhood search as well as guided local search. Furthermore, the integration of meta-heuristics with the branch-and-bound paradigm is investigated.