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.

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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

Good heuristic solutions for large multisource Weber problems can be obtained by solving related p-median problems in which potential locations of the facilities are users locations and then solving Weber problems for the sets of users of each facility. Resume De bonnes solutions heuristiques pour des problemes de Weber Multisources de grande taille peuvent etre obtenues en resolvant des problemes de p-mediane correspondants ou les localisations potentielles des facilites sont les localisations des usagers puis en resolvant des problemes de Weber pour les ensembles d'usagers de chaque facilite.

Heuristic Solution of the Multisource Weber Problem as a p -median Problem

The paper proposes, compares and analyses different memory- based meta-heuristics for the quadratic assignment problem (QAP). Two of these methods (FANT and GDH) are new while two others (HAS-QAP and GTSH) are among the best for structured QAP instances. These methods are based on ant systems and genetic algorithms and they are presented under a unified general scheme, called adaptive memory programming (AMP). However, they use different types of memory and different improving procedures. Two new memoryless methods (VNS-QAP and RVNS-QAP) based on variable neighbourhood search are also proposed and compared with adaptive memory procedures.

/pdf/adaptive-memories-for-the-quadratic-assignment-problems-52qp04elx8.pdf

Adaptive memories for the Quadratic Assignment Problems

The multisource Weber problem is to locate simultaneously p facilities in the Euclidean plane in order to minimize the total transportation cost for satisfying the demand of n fixed users, each supplied from its closest facility. Many heuristics have been proposed for this problem, as well as a few exact algorithms. Heuristics are needed to solve quickly large problems and to provide good initial solutions for exact algorithms. We compare various heuristics, i.e., alternative location- allocation, projection, Tabu search, p-Median plus Weber, Genetic Search and several versions of Variable Neighbourhood Search. It appears that most traditional and some recent heuristics give pour results when the number of facilities to locate is large and that Variable Neighbourhood Search gives consistently best results on average, in moderate computing time.

/pdf/improvements-and-comparison-of-heuristics-for-solving-the-ofhq6ny1h5.pdf

Improvements and Comparison of Heuristics for solving the Multisource Weber Problem

This paper presents a new point of view of ant systems that is more general than previous ones, identifying artificial ants with processes that cooperate through a shared memory and a Queen process that co-ordinates the Ant processes. Then, we present FANT, a new meta-heuristic based on ant systems. The technique is very simple to implement while incorporating a number of search strategies such as intensification, diversification and learning mechanisms. FANT is used to solve various hard assignment problems and shown to be very competitive with other implementations using various meta-heuristics.

Éric D. Taillard

Papers

Heuristic Solution of the Multisource Weber Problem as a p -median Problem

Adaptive memories for the Quadratic Assignment Problems

Improvements and Comparison of Heuristics for solving the Multisource Weber Problem

FANT: Fast ant system

POPMUSIC for the travelling salesman problem