Tabu Search

By Fred W. Glover, Manuel Laguna. 1997

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.

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Citations in the corpus

Neural Combinatorial Optimization with Reinforcement Learning (2016, methodology): “In order to escape poor local optima, ORTools’ local search can also be run in conjunction with different metaheuristics, such as simulated annealing (Kirkpatrick et al., 1983), tabu search (Glover & Laguna, 2013) or guided local search (Voudouris & Tsang, 1999).”
Metaheuristics: review and application (2013, methodology): “…artiﬁcial immune system (AIS; Farmer, Packard, & Perelson, 1986), simulated annealing (SA; Kirkpatrick, Gellat, & Vecchi, 1983), tabu search (TS; Glover & Laguna, 1997), ant colony optimisation (ACO; Dorigo, 1992), particle swarm optimisation (PSO; Kennedy & Eberhart, 1995) and differential…”
Review of job shop scheduling research and its new perspectives under Industry 4.0 (2017, methodology): “(4) Combination algorithms and recent studies based on TS The summary and comparison of this method used in JSP can be found in the book of Glover and Laguna (2013).”
Local branching (2003, background): “Although several heuristics have been proposed in the literature for speciﬁc classes of problems, only a few papers deal with general-purpose MIP heuristics, including [1], [2], [7], [8], [9], [11], [12], [15], [16], and [20] among others.”
Bridging the gap between planning and scheduling (2000, background): “Tabu search [64] has a similar objective: it directly breaks cycling by preventing the return to a recently explored options.”
The feasibility pump (2005, background): “Heuristic approaches to general MIP problems have been proposed by several authors, including [1] [2] [3] [6] [8] [9] [10] [11] [12] [13] [17] [16] [20] [21] [24].”
Reinforcement Learning for Solving the Vehicle Routing Problem (2018, background): “…savings heuristic [9], Sweep heuristic [36], Christoﬁdes’ heuristic [8] and a few others) for ﬁnding an initial solution and metaheuristics (e.g. Guided Local Search [34], Tabu Search [14] and Simulated Annealing [21]) for escaping from local minima in the search for the best solution.”
Greedy Randomized Adaptive Search Procedures. (2003, methodology): “The Proximate Optimality Principle (POP) is based on the idea that “good solutions at one level are likely to be found ‘close to’ good solutions at an adjacent level” [88].”
Beam-ACO - hybridizing ant colony optimization with beam search: an application to open shop scheduling (2005, background): “Examples are tabu search (TS) [7], simulated annealing (SA) [8], and iterated local search (ILS) [9].”
On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks (2016, methodology): “titude of the aerial base stations, makes the problem even more complex. Meta heuristic algorithms such as genetic algorithm [7], simulated annealing [8], particle swarm optimization [9], tabu search [10], and ant colony optimization [11] are often used in such complex problems. Here we use particle”
A multiperiod set covering location model for dynamic redeployment of ambulances (2008, methodology): “The main algorithm uses a tabu search [32] meta-heuristic to select the best sites for servers and Jarvis' hypercube approximation algorithm to compute server specific busy probabilities and the resulting coverage.”