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Tabu Search∗

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Handbook of Combinatorial Optimization

Abstract

Tabu search, also called adaptive memory programming, is a method for solving challenging problems in the field of optimization. The goal is to identify the best decisions or actions in order to maximize some measure of merit (such as maximizing profit, effectiveness, quality, and social or scientific benefit) or to minimize some measure of demerit (cost, inefficiency, waste, and social or scientific loss).

Practical applications in optimization addressed by tabu search are exceedingly challenging and pervade the fields of business, engineering, economics, and science. Everyday examples include problems in resource management, financial and investment planning, healthcare systems, energy and environmental policy, pattern classification, biotechnology, and a host of other areas. The complexity and importance of such problems has motivated a wealth of academic and practical research throughout the past several decades, in an effort to discover methods that are able to find solutions of higher quality than many found in the past and capable of producing such solutions within feasible time limits or at reduced computational cost.

Tabu search has emerged as one of the leading technologies for handling optimization problems that have proved difficult or impossible to solve with classical procedures that dominated the attention of textbooks and were considered the mainstays of available alternatives until recent times. A key feature of tabu search, underscored by its adaptive memory programming alias, is the use of special strategies designed to exploit adaptive memory. The idea is that an effective search for optimal solutions should involve a process of flexibly responding to the solution landscape in a manner that permits it to learn appropriate directions to take along with appropriate departures to explore new terrain. The adaptive memory feature of tabu search allows the implementation of procedures that are capable of searching this terrain economically and effectively.

∗The material of this chapter is in part adapted from the book Tabu Search, by Fred Glover and Manuel Laguna, Kluwer Academic Publishers, 1997.

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Author information

Authors and Affiliations

  1. OptTek Systems, Inc, 2241 17th Street, 80304, Boulder CO, USA

    Fred Glover

  2. Leeds School of Business, University of Colorado, UCB 419, 80309, Boulder CO, USA

    Manuel Laguna

Authors
  1. Fred Glover
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  2. Manuel Laguna
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Correspondence to Fred Glover .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Eng, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. Department of Computer Science, University of Texas, Dallas, Richardson, Texas, USA

    Ding-Zhu Du

  3. Dept. Comp. Sci. & Engineering, University of California, San Diego, La Jolla, California, USA

    Ronald L. Graham

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Glover, F., Laguna, M. (2013). Tabu Search∗ . In: Pardalos, P., Du, DZ., Graham, R. (eds) Handbook of Combinatorial Optimization. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7997-1_17

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