From the Publisher:
This book explores the meta-heuristics approach called tabu search, which is dramatically changing our ability to solve a hostof problems that stretch over the realms of resource planning,telecommunications, VLSI design, financial analysis, scheduling, spaceplanning, energy distribution, molecular engineering, logistics,pattern classification, flexible manufacturing, waste management,mineral exploration, biomedical analysis, environmental conservationand scores of other problems. The major ideas of tabu search arepresented with examples that show their relevance to multipleapplications. Numerous illustrations and diagrams are used to clarifyprinciples that deserve emphasis, and that have not always been wellunderstood or applied. The book's goal is to provide ''hands-on' knowledge and insight alike, rather than to focus exclusively eitheron computational recipes or on abstract themes. This book is designedto be useful and accessible to researchers and practitioners inmanagement science, industrial engineering, economics, and computerscience. It can appropriately be used as a textbook in a masterscourse or in a doctoral seminar. Because of its emphasis on presentingideas through illustrations and diagrams, and on identifyingassociated practical applications, it can also be used as asupplementary text in upper division undergraduate courses. 

Finally, there are many more applications of tabu search than canpossibly be covered in a single book, and new ones are emerging everyday. The book's goal is to provide a grounding in the essential ideasof tabu search that will allow readers to create successfulapplications of their own. Along with the essentialideas,understanding of advanced issues is provided, enabling researchers togo beyond today's developments and create the methods of tomorrow.

Tabu Search

List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

/pdf/evolutionary-algorithms-for-solving-multi-objective-problems-2txxk4mqci.pdf

Evolutionary algorithms for solving multi-objective problems

https://sci2s.ugr.es/sites/default/files/files/Teaching/GraduatesCourses/Metaheuristicas/Bibliography/VNS.pdf

Variable neighborhood search

Variable Neighborhood Search.

Boolean satisfiability is probably the most studied of the combinatorial optimization/search problems. Significant effort has been devoted to trying to provide practical solutions to this problem for problem instances encountered in a range of applications in electronic design automation (EDA), as well as in artificial intelligence (AI). This study has culminated in the development of several SAT packages, both proprietary and in the public domain (e.g. GRASP, SATO) which find significant use in both research and industry. Most existing complete solvers are variants of the Davis-Putnam (DP) search algorithm. In this paper we describe the development of a new complete solver, Chaff which achieves significant performance gains through careful engineering of all aspects of the search-especially a particularly efficient implementation of Boolean constraint propagation (BCP) and a novel low overhead decision strategy. Chaff has been able to obtain one to two orders of magnitude performance improvement on difficult SAT benchmarks in comparison with other solvers (DP or otherwise), including GRASP and SATO.

/pdf/chaff-engineering-an-efficient-sat-solver-2ima1ekzuu.pdf

Chaff: engineering an efficient SAT solver

System und Verfahren zur Bestimmung des Positionsbereichs eines mobilen Benutzers

An unsupervised machine learning method based on association rule is studied for the Quadratic Assignment Problem. Parallel extraction of itemsets and local search algorithms are proposed. The extraction of frequent itemsets in the context of local search is shown to produce good results for a few problem instances. Negative results of the proposed learning mechanism are reported for other instances. This result contrasts with other hard optimization problems for which efficient learning processes are known in the context of local search. 

Unsupervised Machine Learning for the Quadratic Assignment Problem

One of the challenging aspects in metaheuristics design is an adequate definition of solution quality , which has both cost and structural properties. Cost is always measured by an objective function, while structural properties are reflected by values of decision variables. From the application of successful metaheuristics, first and foremost scatter search (SCS, see [1]), it is well-known that structural properties play an import role when a set of solutions has to be evaluated during the course of the optimization. Those pooled solutions may be treated in a simultaneous way, e.g. in the so-called reference set of an SCS, or subsequently in multi(re)start approaches, which means that new search trajectories are initialized upon solutions which have been proved to incorporate a higher quality and have been recognized as being elite during the search history. Algorithms which are evaluating solutions upon their distance to each other or to the incumbent best-known solution have proved to be very effective in the context of selecting or rejecting solutions (or their elements) (e.g. see [2, 3]). In the current paper we want to concentrate on the quadratic assignment problem (QAP). The intended goal is a twofold one: firstly, the main focus lies on a methodic and statistical analysis, which evaluates the use of a permutation distance measure in the context of the QAP. Secondly, based on the preceding experimental findings, we try to design some useful heuristic components in order to improve a standard metaheuristic algorithm. The basis is the established robust tabu search code by Taillard [4].

/pdf/on-the-use-of-permutation-distances-in-metaheuristics-ptt43ra4q1.pdf

On the use of permutation distances in metaheuristics

This work, outlined in Alvim and Taillard [1], address the point feature label placement problem (PFLP) which is the problem of placing text labels adjacent to point features on a map so as to maximize legibility. We consider a set of n points, each one with p candidate label positions. A solution S is a list of n labels. For any S, we denote by f(S) the function that counts the number of point features labeled with one or more overlaps (in other words, the number of labels with conflicts) and by c(S) the function that counts the number of overlaps. The goal is to minimize c(S). Cartographic preferences also can be taken into account. For p ≥ 4, the PFLP is NP-hard [2]. With increasing use of electronic maps, fast and good labeling algorithms must be designed. The POPMUSIC approach proposed in [1] is analyzed under a practical complexity point of view. Computational time measures confirm that our POPMUSIC approach typically runs in O(n · p log(n · p)) while producing solution of higher quality than any other heuristic approach previously proposed.

A POPMUSIC heuristic for the Point Feature Label Placement Problem

The increasing availability of data in our information society has led to the need for valid tools for its modelling and analysis. Data-Mining is the process designed to explore large amounts of business, family, or institution data in order to discover interesting models and patterns. The process can be decomposed in three main steps: acquisition, preprocessing and analysis of data. For each of the step we developed efficient and robust C libraries to manage, organise, transform and analyse huge amounts of data.

/pdf/preprocessing-and-clustering-large-scaled-data-mining-37y7f4697i.pdf

Éric D. Taillard

Papers

System und Verfahren zur Bestimmung des Positionsbereichs eines mobilen Benutzers

Unsupervised Machine Learning for the Quadratic Assignment Problem

On the use of permutation distances in metaheuristics

A POPMUSIC heuristic for the Point Feature Label Placement Problem

Preprocessing and Clustering large-scaled Data-Mining problem instances