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

The Recursive Deterministic Perceptron (RDP) feed-forward multilayer neural network is a generalisation of the single layer perceptron topology. This model is capable of solving any two-class classification problem as opposed to the single layer perceptron which can only solve classification problems dealing with linearly separable sets (two classes X and Y of IR are said to be linearly separable if there exists a hyperplane such that the elements of X and Y lie on the two opposite sides of IR delimited by this hyperplane). For all classification problems, the construction of an RDP is done automatically and convergence is always guaranteed. Three methods for constructing RDP neural networks exist: Batch, Incremental, and Modular. The Batch method has been extensively tested. However, no testing has been done before on the Incremental and Modular methods. Contrary to the Batch method, the complexity of these two methods is not NPComplete. A study on the three methods is presented. This study will allow the highlighting of the main advantages and disadvantages of each of these methods by comparing the results obtained while building RDP neural networks with the three methods in terms of the level of generalisation. The networks were trained and tested using the following standard benchmark classification datasets: IRIS and SOYBEAN.

Generalisation and the recursive deterministic perceptron.

The Recursive Deterministic Perceptron (RDP) feed-forward multilayer neural network is a generalisation of the single layer perceptron topology. This model is capable of solving any two-class classification problem as opposed to the single layer perceptron which can only solve classification problems dealing with linearly separable sets (two classes X and Y of IRdare said to be linearly separable if there exists a hyperplane such that the elements of X and Y lie on the two opposite sides of IRddelimited by this hyperplane). For all classification problems, the construction of an RDP is done automatically and convergence is always guaranteed. Three methods for constructing RDP neural networks exist: Batch, Incremental, and Modular. The Batch method has been extensively tested. However, no testing has been done before on the Incremental and Modular methods. Contrary to the Batch method, the complexity of these two methods is not NP-Complete. A study on the three methods is presented. This study will allow the highlighting of the main advantages and disadvantages of each of these methods by comparing the results obtained while building RDP neural networks with the three methods in terms of the level of generalisation. The networks were trained and tested using the following standard benchmark classification datasets: IRIS and SOYBEAN.

The Generalisation of the Recursive Deterministic Perceptron

The main focus of this chapter is to introduce a new way to ease the integration of labelling placement functionalities from a given library: a programming-language independent solution based on a web service wrapper. Considering the GeoWeb allowing to combine geodata, mapping, and geoprocessing services to fulfil various needs, it is to give an answer to a current lack: there is no such an approach for delivering advanced labelling functionalities through a standardized web service. The chapter describes a solution to wrap PAL library using standards from Open Geospatial Consortium like Web Processing Service and Symbology Encoding.

Towards an Automated Label Placement Service

Memory: The memory (or data warehouse) is constituted of solutions. Each solution is classed into 3 categories : elite, intermediate and bad solutions. Each solution contains several components (for the TSP, a component is an edge: a route from one city to the next one). So, each component can be classified into 0, 1 or several of the 3 classes (no solution of the memory contains a given component, a component belongs to solutions of a single class or a component belongs to solutions of several classes). Memory initialization: The memory is initialized with solutions created with the the Quick-Boruvka procedure, as implemented in the Concorde software. These solutions are improved with the Chained Lin-Kernighan (CLK) procedure implemented in the Concorde software. Noising: Before launching CLK, the length of each edge is perturbed by a value that depends on av alue 1 >r> 0. This value r linearly decreases with the iteration number. For perturbing the length of the edges, we first multiply this length by a factor uniformly distributed between 1 � r and 1 + r. If the edge only belongs to elite solutions, the perturbed length is then diminished by a factor 1 � r. If the edge only belongs to bad solutions, the perturbed length is then increased by a factor 1 +r. Building a new solution: The quickest way to build a new solution is to start from a solution already in memory. So, we took the best solution stored in memory. Since the length of

http://mistic.heig-vd.ch/taillard/articles.dir/mic07_0106.pdf

Parallel noising methods embedded in an adaptive memory

This open access book demonstrates didactically all the steps for designing heuristic algorithms for difficult optimization. 

Éric D. Taillard

Papers

Generalisation and the recursive deterministic perceptron.

The Generalisation of the Recursive Deterministic Perceptron

Towards an Automated Label Placement Service

Parallel noising methods embedded in an adaptive memory

Design of Heuristic Algorithms for Hard Optimization: With Python Codes for the Travelling Salesman Problem