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Tabu Search  Part I
, 1989
"... This paper presents the fundamental principles underlying tabu search as a strategy for combinatorial optimization problems. Tabu search has achieved impressive practical successes in applications ranging from scheduling and computer channel balancing to cluster analysis and space planning, and more ..."
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Cited by 680 (11 self)
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This paper presents the fundamental principles underlying tabu search as a strategy for combinatorial optimization problems. Tabu search has achieved impressive practical successes in applications ranging from scheduling and computer channel balancing to cluster analysis and space planning, and more recently has demonstrated its value in treating classical problems such as the traveling salesman and graph coloring problems. Nevertheless, the approach is still in its infancy, and a good deal remains to be discovered about its most effective forms of implementation and about the range of problems for which it is best suited. This paper undertakes to present the major ideas and findings to date, and to indicate challenges for future research. Part I of this study indicates the basic principles, ranging from the shortterm memory process at the core of the search to the intermediate and long term memory processes for intensifying and diversifying the search. Included are illustrative data structures for implementing the tabu conditions (and associated aspiration criteria) that underlie these processes. Part I concludes with a discussion of probabilistic tabu search and a summary of computational experience for a variety of applications. Part I1 of this study (to appear in a subsequent issue) examines more advanced considerations, applying the basic ideas to special settings and outlining a dynamic move structure to insure finiteness. Part I1 also describes tabu search methods for solving mixed integer programming problems and gives a brief summary of additional practical experience, including the use of tabu search to guide other types of processes, such as those
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
 ACM COMPUTING SURVEYS
, 2003
"... 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 meta ..."
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Cited by 314 (17 self)
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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 behaviour 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.
A User’s Guide to Tabu Search “,
 Annals of Oper. Reas.,
, 1993
"... Abstract We describe the main features of tabu search, emphasizing a perspective for guiding a user to widerstand basic implementation principles for solving combinatorial or nonlinear problems. We also identify recent developments and extensions that have contributed to increasing the efficiency o ..."
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Cited by 181 (2 self)
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Abstract We describe the main features of tabu search, emphasizing a perspective for guiding a user to widerstand basic implementation principles for solving combinatorial or nonlinear problems. We also identify recent developments and extensions that have contributed to increasing the efficiency of the method. One of the useful aspects of tabu search is the ability to adapt a rudimentary prototype implementation to encompass additional model elements, such as new types of constraints and objective functions. Similarly, the method itself can be evolved to varying levels of sophistication. We provide several examples of discrete optimization problems to illustrate the strategic concerns of tabu search, and to show how they may be exploited in various contexts. Our presentation is motivated by the emergence of an extensive literature of computational results, which demonstrates that a welllWled implementation makes it possible to obtain solutions of high quality for difficult problems, yielding outcomes in some settings that have not been matched by other known techniques.
PseudoBoolean Optimization
 DISCRETE APPLIED MATHEMATICS
, 2001
"... This survey examines the state of the art of a variety of problems related to pseudoBoolean optimization, i.e. to the optimization of set functions represented by closed algebraic expressions. The main parts of the survey examine general pseudoBoolean optimization, the specially important case of ..."
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Cited by 179 (5 self)
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This survey examines the state of the art of a variety of problems related to pseudoBoolean optimization, i.e. to the optimization of set functions represented by closed algebraic expressions. The main parts of the survey examine general pseudoBoolean optimization, the specially important case of quadratic pseudoBoolean optimization (to which every pseudoBoolean optimization can be reduced), several other important special classes, and approximation algorithms.
Heuristics for cardinality constrained portfolio optimisation
, 2000
"... In this paper we consider the problem of finding the efficient frontier associated with the standard meanvariance portfolio optimisation model. We extend the standard model to include cardinality constraints that limit a portfolio to have a specified number of assets, and to impose limits on the pr ..."
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Cited by 99 (4 self)
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In this paper we consider the problem of finding the efficient frontier associated with the standard meanvariance portfolio optimisation model. We extend the standard model to include cardinality constraints that limit a portfolio to have a specified number of assets, and to impose limits on the proportion of the portfolio held in a given asset (if any of the asset is held). We illustrate the differences that arise in the shape of this efficient frontier when such constraints are present. We present three heuristic algorithms based upon genetic algorithms, tabu search and simulated annealing for finding the cardinality constrained efficient frontier. Computational results are presented for five data sets involving up to 225 assets.
Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems
 SIAM Journal on Optimization
, 2004
"... A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for gene ..."
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Cited by 55 (6 self)
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A new class of algorithms for solving nonlinearly constrained mixed variable optimization problems is presented. This class combines and extends the AudetDennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPSfilter algorithms for general nonlinear constraints. In generalizing existing algorithms, new theoretical convergence results are presented that reduce seamlessly to existing results for more specific classes of problems. While no local continuity or smoothness assumptions are required to apply the algorithm, a hierarchy of theoretical convergence results based on the Clarke calculus is given, in which local smoothness dictate what can be proved about certain limit points generated by the algorithm. To demonstrate the usefulness of the algorithm, the algorithm is applied to the design of a loadbearing thermal insulation system. We believe this is the first algorithm with provable convergence results to directly target this class of problems.
Heuristic Algorithms for the Unconstrained Binary Quadratic Programming Problem
, 1998
"... In this paper we consider the unconstrained binary quadratic programming problem. This is the problem of maximising a quadratic objective by suitable choice of binary (zeroone) variables. We present two heuristic algorithms based upon tabu search and simulated annealing for this problem. Computatio ..."
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Cited by 42 (0 self)
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In this paper we consider the unconstrained binary quadratic programming problem. This is the problem of maximising a quadratic objective by suitable choice of binary (zeroone) variables. We present two heuristic algorithms based upon tabu search and simulated annealing for this problem. Computational results are presented for a number of publically available data sets involving up to 2500 variables. An interesting feature of our results is that whilst for most problems tabu search dominates simulated annealing for the very largest problems we consider the converse is true. This paper typifies a "multiple solution technique, single paper" approach, i.e. an approach that within the same paper presents results for a number of different heuristics applied to the same problem. Issues relating to algorithmic design for such papers are discussed. Keywords: unconstrained binary (zeroone) quadratic programming
Genetic algorithms and tabu search: hybrids for optimization
 Comput. Oper. Res
, 1995
"... Scope and Purpo~The development of hybrid procedures for optimization focuses on enhancing the strengths and compensating for the weaknesses of two or more complementary approaches. The goal is to intelligently combine the key elements of competing methodologies to create a superior solution proce ..."
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Cited by 39 (1 self)
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Scope and Purpo~The development of hybrid procedures for optimization focuses on enhancing the strengths and compensating for the weaknesses of two or more complementary approaches. The goal is to intelligently combine the key elements of competing methodologies to create a superior solution procedure. Our paper explores the marriage between tabu search and genetic algorithms in the context of solving difficult optimization problems. Among other ideas, the procedure known as scatter search is revisited to create a unifying environment where tabu search and genetic algorithms can coexist. Overall, our objective is to demonstrate that it is possible to establish useful connections between methods whose search principles may superficially appear unrelated. AbstractGenetic algorithms and tabu search have a number of significant differences. They also have some common bonds, often unrecognized. We explore the nature of the connections between the methods, and show that a variety of opportunities exist for creating hybrid approaches to take advantage of their complementary features. Tabu search has pioneered the systematic exploration of memory functions in search processes, while genetic algorithms have pioneered the implementation of methods that exploit the idea of combining solutions. There is also another approach, related to both of these, that is frequently overlooked. The procedure called scatter search, whose origins overlap with those of tabu search (and
A LocationBased Routing Method for Mobile Ad Hoc Networks
 IEEE Trans. Mob. Comput
, 2005
"... Abstract—Using location information to help routing is often proposed as a means to achieve scalability in large mobile ad hoc networks. However, locationbased routing is difficult when there are holes in the network topology and nodes are mobile or frequently disconnected to save battery. Terminod ..."
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Cited by 31 (0 self)
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Abstract—Using location information to help routing is often proposed as a means to achieve scalability in large mobile ad hoc networks. However, locationbased routing is difficult when there are holes in the network topology and nodes are mobile or frequently disconnected to save battery. Terminode routing, presented here, addresses these issues. It uses a combination of locationbased routing (Terminode Remote Routing, TRR), used when the destination is far, and link state routing (Terminode Local Routing, TLR), used when the destination is close. TRR uses anchored paths, a list of geographic points (not nodes) used as loose source routing information. Anchored paths are discovered and managed by sources, using one of two low overhead protocols: Friend Assisted Path Discovery and Geographical Mapbased Path Discovery. Our simulation results show that terminode routing performs well in networks of various sizes. In smaller networks, the performance is comparable to MANET routing protocols. In larger networks that are not uniformly populated with nodes, terminode routing outperforms existing locationbased or MANET routing protocols. Index Terms—Restricted random waypoint, mobility model, ad hoc network, scalable routing, locationbased routing method,
Analyzing and modeling the maximum diversity problem by zeroone programming. Decision Sci
, 1993
"... The problem of maximizing diversity deals with selecting a set of elements from some larger collection such that the selected elements exhibit the greatest variety of characteristics. A new model is proposed in which the concept of diversity is quantifiable and measurable. A quadratic zeroone model ..."
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Cited by 28 (3 self)
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The problem of maximizing diversity deals with selecting a set of elements from some larger collection such that the selected elements exhibit the greatest variety of characteristics. A new model is proposed in which the concept of diversity is quantifiable and measurable. A quadratic zeroone model is formulated for diversity maximization. Based upon the formulation, it is shown that the maximum diversity problem is NPhard. 'Tho equivalent linear integer programs are then presented that offer progressively greater computational efficiency. Another formulation is also introduced which involves a different diversity objective. An example is given to illustrate how additional considerations can be incorporated into the maximum diversity model. Subject Areas: Discnk hgmmming, Linear Rvgmmming, and Mathematical hgmmming.