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

The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.

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Metaheuristics in combinatorial optimization: Overview and conceptual comparison

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.

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Chaff: engineering an efficient SAT solver

computational bounds for local search in combinatorial local search algorithms for combinatorial problems local search algorithms for solving the combinatorial a dual local search framework for combinatorial the max-min ant system and local search for combinatorial a framework for local combinatorial optimization problems local search in combinatorial optimization mifou local search in combinatorial optimization radarx heuristics and local search paginas.fe.up local search in combinatorial optimization banani local search in combinatorial optimization integrating interval estimates of global optima and local local search in combinatorial optimization holbarto a fuzzy valuation-based local search framework for local search in combinatorial optimization gbv gentle introduction to local search in combinatorial e cient local search for several combinatorial introduction: combinatorial problems and search on application of the local search optimization online local search for combinatorial optimisation problems how to choose solutions for local search in multiobjective a hybrid of inference and local search for distributed on set-based local search for multiobjective combinatorial localsolver: black-box local search for combinatorial on local optima in multiobjective combinatorial sets of interacting scalarization functions in local introduction e r optimization online estimation-based local search for stochastic combinatorial advanced search combinatorial problems sas/or user's guide: local search optimization how easy is local search? univerzita karlova metaheuristic search for combinatorial optimization local search genetic algorithms for the job shop towards a formalization of combinatorial local search dynamic and adaptive neighborhood search in combinatorial global search in combinatorial optimization using a framework for the development of local search heuristics a feasibility-preserving local search operator for hybrid metaheuristics in combinatorial optimization: a survey consultant-guided search algorithms with local search for a new optimization algorithm for combinatorial problems hybrid metaheuristics in combinatorial optimization: a survey network algorithms colorado state university model-based search for combinatorial optimization: a local and global optimization stochastic local search algorithms for multiobjective corso (collaborative reactive search optimization local search in combinatorial optimization zaraa

Local Search in Combinatorial Optimization

A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem

We present several technologies recommended for making 2-dimensional (2D) or 3-dimensional (3D) representations of selected discrete solution spaces as well as features of the solution algorithms occurring in combinatorial optimization (CO) tasks. We provide some results of theoretical as well as experimental investigations of so called landscape of the space with reference to some exemplary hard CO problems. Theoretical analysis starts from various measures of the distance between solutions represented by typical combinatorial objects, namely permutations, composition of permutations, set partition and so forth. Then, we propose some mapping of n-dimensional space of permutations into 2- or 3 dimensional Euclidean space to extract factors responsible for hardness of the problem and to illustrate behavior of approximation algorithms.

Visualization of Solution Spaces for the Needs of Metaheuristics

In the paper we present some methods of empirical research of optimization problems’ solution space, for which solutions are represented by permutations. Sampling the feasible solutions set we determine a histogram of frequency of incidence of local minima measuring its distance to the neighborhood graph’s center. On its basis we verify a statistical hypothesis on normal distribution occurrence of local minima. Due to this research we can significantly reduce the area of the searching process during a local search metaheuristics work, focusing the searching process on Big Valley. We propose an algorithm with changeable diameter of the search.

Local Search Metaheuristics with Reduced Searching Diameter

We consider a general class of NP-hard combinatorial optimization problems with solutions represented by permutations. Assuming that each problem is solved then by suitable approximate method based on local search or population based approaches, the landscape analysis of the solution space is carried out. Trajectories performed by various algorithms differ each other and can be controlled by parameters derived from the image. The topic of space images appears seldom in the literature, so at the beginning we refer shortly to the state-of-the-art on this subject. Then we propose our own original concepts of space imaging. As an image we are taking the two-dimensional and three-dimensional Euclidean cube and the sphere. Necessary distance measures in the solution space and images space are provided. The new method of visualization is proposed based on reference points derived from the specific auxiliary optimization task. The method is used to track manifold search trajectories performed in the solution space. Variability of the images is illustrated by few experimental research.

Landscape Imaging of the Discrete Solution Space

The paper reports very recent results dealing with a prevalent case of so called cyclical manufacturing/production systems supplying various products in lots with inter-lots setup times. Setups appear between series of different goods. We propose a convenient approach for modeling and approximate solving, which uses our previous findings developed already for classical systems occurring in operations research (OR) field, namely job-shop scheduling problem with setups, as well as those proved for cyclical job-shop. Analysis is carried out for a general structure of manufacturing system, namely the job-shop scheduling problem because of practical applicability of this model. We consider chiefly the modelling aspect by using combinatorial representation of a solution with the support of unique class of graphs. We propose the method of finding/evaluating the minimal cycle time for given processing order by using the graph and some special features of the problem.

Cyclic Scheduling of Lots with Setup Times

In the work there is presented a comprehensive overview of scientific and research directions accomplished by the Author and cooperating team in recent years. The team is working in the area of modeling and optimization dedicated for the implementation in discrete manufacturing systems, transport systems, warehousing systems etc. The immediate result of the these research is the original uniform approach to design of various algorithms for problems derived from the industry. Foundations of the approach combine not only graphs, operations research, combinatorial optimization, mathematical programming, but also modern solution technologies and powerful computing tools. In the review there are discussed general technologies of discrete processes modeling as well as the evolution of solving methods in the recent fifty years. There are some selected fundamental discrete optimization problems discussed in relation to the landscape of the solution space. There are also collected the main theoretical results obtained by the Author, his leading publications as well as the spectacular algorithms developed so far. The future research directions are also outlined. The presentation is illustrated by references to papers of the Author and the bibliography of collaborators.

Czesław Smutnicki

Papers

Visualization of Solution Spaces for the Needs of Metaheuristics

Local Search Metaheuristics with Reduced Searching Diameter

Landscape Imaging of the Discrete Solution Space

Cyclic Scheduling of Lots with Setup Times

Discrete Optimization in the Industrial Computer Science