The Experts below are selected from a list of 3432 Experts worldwide ranked by ideXlab platform
Thomas Stutzle - One of the best experts on this subject based on the ideXlab platform.
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Iterated Local Search: Framework and Applications
Handbook of Metaheuristics, 2010Co-Authors: Helena R Lourenco, Olivier Martin, Thomas StutzleAbstract:The key idea underlying Iterated Local Search is to focus the Search not on the full space of all candidate solutions but on the solutions that are returned by some underlying algorithm, typically a Local Search heuristic. The resulting Search behavior can be characterized as iteratively building a chain of solutions of this embedded algorithm. The result is also a conceptually simple metaheuristic that nevertheless has led to state-of-the-art algorithms for many computationally hard problems. In fact, very good performance is often already obtained by rather straightforward implementations of the metaheuristic. In addition, the modular architecture of Iterated Local Search makes it very suitable for an algorithm engineering approach where, progressively, the algorithm’s performance can be further optimized. Our purpose here is to give an accessible description of the underlying principles of Iterated Local Search and a discussion of the main aspects that need to be taken into account for a successful application of it. In addition, we review the most important applications of this method and discuss its relationship with other metaheuristics.
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hierarchical Iterated Local Search for the quadratic assignment problem
Lecture Notes in Computer Science, 2009Co-Authors: Mohamed Saifullah Hussin, Thomas StutzleAbstract:Iterated Local Search is a stochastic Local Search (SLS) method that combines a perturbation step with an embedded Local Search algorithm. In this article, we propose a new way of hybridizing Iterated Local Search. It consists in using an Iterated Local Search as the embedded Local Search algorithm inside another Iterated Local Search. This nesting of Local Searches and Iterated Local Searches can be further Iterated, leading to a hierarchy of Iterated Local Searches. In this paper, we experimentally examine this idea applying it to the quadratic assignment problem. Experimental results on large, structured instances show that the hierarchical Iterated Local Search can offer advantages over using a "flat" Iterated Local Search and make it a promising technique to be further considered for other applications.
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Hybrid Metaheuristics - Hierarchical Iterated Local Search for the Quadratic Assignment Problem
Hybrid Metaheuristics, 2009Co-Authors: Mohamed Saifullah Hussin, Thomas StutzleAbstract:Iterated Local Search is a stochastic Local Search (SLS) method that combines a perturbation step with an embedded Local Search algorithm. In this article, we propose a new way of hybridizing Iterated Local Search. It consists in using an Iterated Local Search as the embedded Local Search algorithm inside another Iterated Local Search. This nesting of Local Searches and Iterated Local Searches can be further Iterated, leading to a hierarchy of Iterated Local Searches. In this paper, we experimentally examine this idea applying it to the quadratic assignment problem. Experimental results on large, structured instances show that the hierarchical Iterated Local Search can offer advantages over using a "flat" Iterated Local Search and make it a promising technique to be further considered for other applications.
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An application of Iterated Local Search to the Graph Coloring Problem
2007Co-Authors: Marco Chiarandini, Thomas StutzleAbstract:Graph coloring is a well known problem from graph theory that, when solving it with Local Search algorithms, is typically treated as a series of constraint satisfaction problems: for a given number of colors k, one has to find a feasible coloring; once such a coloring is found, the number of colors is decreased and the Local Search starts again. Here we explore the application of Iterated Local Search to the graph coloring problem. Iterated Local Search is a simple and powerful metaheuristic that has shown very good results for a variety of optimization problems. In our reSearch we investigate different perturbation schemes and present computational results on some hard instances from the DIMACS benchmark suite.
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Iterated Local Search for the quadratic assignment problem
European Journal of Operational Research, 2006Co-Authors: Thomas StutzleAbstract:Iterated Local Search (ILS) is a simple and powerful stochastic Local Search method. This article presents and analyzes the application of ILS to the quadratic assignment problem (QAP). We justify the potential usefulness of an ILS approach to this problem by an analysis of the QAP Search space. However, an analysis of the run-time behavior of a basic ILS algorithm reveals a stagnation behavior which strongly compromises its performance. To avoid this stagnation behavior, we enhance the ILS algorithm using acceptance criteria that allow moves to worse Local optima and we propose population-based ILS extensions. An experimental evaluation of the enhanced ILS algorithms shows their excellent performance when compared to other state-of-the-art algorithms for the QAP.
Peter Merz - One of the best experts on this subject based on the ideXlab platform.
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EvoCOP - Iterated Local Search for Minimum Power Symmetric Connectivity in Wireless Networks
Evolutionary Computation in Combinatorial Optimization, 2009Co-Authors: Steffen Wolf, Peter MerzAbstract:The problem of finding a symmetric connectivity topology with minimum power consumption in a wireless ad-hoc network is NP-hard. This work presents a new Iterated Local Search to solve this problem by combining filtering techniques with Local Search. The algorithm is benchmarked using instances with up to 1000 nodes, and results are compared to optimal or best known results as well as other heuristics. For these instances, the proposed algorithm is able to find optimal and near-optimal solutions and outperforms previous heuristics.
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improved construction heuristics and Iterated Local Search for the routing and wavelength assignment problem
European conference on Evolutionary Computation in Combinatorial Optimization, 2008Co-Authors: Kerstin Bauer, Thomas Fischer, Sven O Krumke, Katharina Gerhardt, Stephan Westphal, Peter MerzAbstract:This paper deals with the design of improved construction heuristics and Iterated Local Search for the Routing and Wavelength Assignment problem (RWA). Given a physical network and a set of communication requests, the static RWA deals with the problem of assigning suitable paths and wavelengths to the requests. We introduce benchmark instances from the SND library to the RWA and argue that these instances are more challenging than previously used random instances. We analyze the properties of several instances in detail and propose an improved construction heuristic to handle 'problematic' instances. Our Iterated Local Search finds the optimum for most instances.
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EvoCOP - Improved construction heuristics and Iterated Local Search for the routing and wavelength assignment problem
Evolutionary Computation in Combinatorial Optimization, 2008Co-Authors: Kerstin Bauer, Thomas Fischer, Sven O Krumke, Katharina Gerhardt, Stephan Westphal, Peter MerzAbstract:This paper deals with the design of improved construction heuristics and Iterated Local Search for the Routing and Wavelength Assignment problem (RWA). Given a physical network and a set of communication requests, the static RWA deals with the problem of assigning suitable paths and wavelengths to the requests. We introduce benchmark instances from the SND library to the RWA and argue that these instances are more challenging than previously used random instances. We analyze the properties of several instances in detail and propose an improved construction heuristic to handle 'problematic' instances. Our Iterated Local Search finds the optimum for most instances.
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an Iterated Local Search approach for minimum sum of squares clustering
Intelligent Data Analysis, 2003Co-Authors: Peter MerzAbstract:Since minimum sum-of-squares clustering (MSSC) is an NP-hard combinatorial optimization problem, applying techniques from global optimization appears to be promising for reliably clustering numerical data. In this paper, concepts of combinatorial heuristic optimization are considered for approaching the MSSC: An Iterated Local Search (ILS) approach is proposed which is capable of finding (near-)optimum solutions very quickly. On gene expression data resulting from biological microarray experiments, it is shown that ILS outperforms multi–start k-means as well as three other clustering heuristics combined with k-means.
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IDA - An Iterated Local Search Approach for Minimum Sum-of-Squares Clustering
Advances in Intelligent Data Analysis V, 2003Co-Authors: Peter MerzAbstract:Since minimum sum-of-squares clustering (MSSC) is an NP-hard combinatorial optimization problem, applying techniques from global optimization appears to be promising for reliably clustering numerical data. In this paper, concepts of combinatorial heuristic optimization are considered for approaching the MSSC: An Iterated Local Search (ILS) approach is proposed which is capable of finding (near-)optimum solutions very quickly. On gene expression data resulting from biological microarray experiments, it is shown that ILS outperforms multi–start k-means as well as three other clustering heuristics combined with k-means.
Dirk Sudholt - One of the best experts on this subject based on the ideXlab platform.
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analysis of an Iterated Local Search algorithm for vertex coloring
International Symposium on Algorithms and Computation, 2010Co-Authors: Dirk Sudholt, Christine ZargesAbstract:Hybridizations of evolutionary algorithms and Local Search are among the best-performing algorithms for vertex coloring. However, the theoretical knowledge about these algorithms is very limited and it is agreed that a solid theoretical foundation is needed. We consider an Iterated Local Search algorithm that iteratively tries to improve a coloring by applying mutation followed by Local Search. We investigate the capabilities and the limitations of this approach using bounds on the expected number of iterations until an optimal or near-optimal coloring is found. This is done for two different mutation operators and for different graph classes: bipartite graphs, sparse random graphs, and planar graphs.
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ISAAC (1) - Analysis of an Iterated Local Search Algorithm for Vertex Coloring
Algorithms and Computation, 2010Co-Authors: Dirk Sudholt, Christine ZargesAbstract:Hybridizations of evolutionary algorithms and Local Search are among the best-performing algorithms for vertex coloring. However, the theoretical knowledge about these algorithms is very limited and it is agreed that a solid theoretical foundation is needed. We consider an Iterated Local Search algorithm that iteratively tries to improve a coloring by applying mutation followed by Local Search. We investigate the capabilities and the limitations of this approach using bounds on the expected number of iterations until an optimal or near-optimal coloring is found. This is done for two different mutation operators and for different graph classes: bipartite graphs, sparse random graphs, and planar graphs.
Ann Nowe - One of the best experts on this subject based on the ideXlab platform.
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fair share ils a simple state of the art Iterated Local Search hyperheuristic
Genetic and Evolutionary Computation Conference, 2014Co-Authors: Steven Adriaensen, Tim Brys, Ann NoweAbstract:In this work we present a simple state-of-the-art selection hyperheuristic called Fair-Share Iterated Local Search (FS-ILS). FS-ILS is an Iterated Local Search method using a conservative restart condition. Each iteration, a perturbation heuristic is selected proportionally to the acceptance rate of its previously proposed candidate solutions (after iterative improvement) by a domain-independent variant of the Metropolis condition. FS-ILS was developed in prior work using a semi-automated design approach. That work focused on how the method was found, rather than the method itself. As a result, it lacked a detailed explanation and analysis of the method, which will be the main contribution of this work. In our experiments we analyze FS-ILS's parameter sensitivity, accidental complexity and compare it to the contestants of the CHeSC (2011) competition.
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GECCO - Fair-share ILS: a simple state-of-the-art Iterated Local Search hyperheuristic
Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014Co-Authors: Steven Adriaensen, Tim Brys, Ann NoweAbstract:In this work we present a simple state-of-the-art selection hyperheuristic called Fair-Share Iterated Local Search (FS-ILS). FS-ILS is an Iterated Local Search method using a conservative restart condition. Each iteration, a perturbation heuristic is selected proportionally to the acceptance rate of its previously proposed candidate solutions (after iterative improvement) by a domain-independent variant of the Metropolis condition. FS-ILS was developed in prior work using a semi-automated design approach. That work focused on how the method was found, rather than the method itself. As a result, it lacked a detailed explanation and analysis of the method, which will be the main contribution of this work. In our experiments we analyze FS-ILS's parameter sensitivity, accidental complexity and compare it to the contestants of the CHeSC (2011) competition.
Christine Zarges - One of the best experts on this subject based on the ideXlab platform.
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analysis of an Iterated Local Search algorithm for vertex coloring
International Symposium on Algorithms and Computation, 2010Co-Authors: Dirk Sudholt, Christine ZargesAbstract:Hybridizations of evolutionary algorithms and Local Search are among the best-performing algorithms for vertex coloring. However, the theoretical knowledge about these algorithms is very limited and it is agreed that a solid theoretical foundation is needed. We consider an Iterated Local Search algorithm that iteratively tries to improve a coloring by applying mutation followed by Local Search. We investigate the capabilities and the limitations of this approach using bounds on the expected number of iterations until an optimal or near-optimal coloring is found. This is done for two different mutation operators and for different graph classes: bipartite graphs, sparse random graphs, and planar graphs.
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ISAAC (1) - Analysis of an Iterated Local Search Algorithm for Vertex Coloring
Algorithms and Computation, 2010Co-Authors: Dirk Sudholt, Christine ZargesAbstract:Hybridizations of evolutionary algorithms and Local Search are among the best-performing algorithms for vertex coloring. However, the theoretical knowledge about these algorithms is very limited and it is agreed that a solid theoretical foundation is needed. We consider an Iterated Local Search algorithm that iteratively tries to improve a coloring by applying mutation followed by Local Search. We investigate the capabilities and the limitations of this approach using bounds on the expected number of iterations until an optimal or near-optimal coloring is found. This is done for two different mutation operators and for different graph classes: bipartite graphs, sparse random graphs, and planar graphs.
