Tabu Search—Part I

TLDR

Tabu search has achieved successes in scheduling, channel balancing, cluster analysis, space planning, and classic problems such as the traveling salesman and graph coloring, yet it remains in its infancy and its most effective implementations and suitable problem domains are still largely unknown. This paper presents the fundamental principles of tabu search, summarizes the major ideas and findings to date, and outlines challenges for future research. Part I details the basic principles of short‑term, intermediate, and long‑term memory processes, including data structures for tabu conditions and aspiration criteria, while Part II extends these concepts to advanced settings, dynamic move structures, mixed‑integer programming, and neural‑network guidance. The study discusses probabilistic tabu search, reports computational experience across various applications, and summarizes practical results, including its use to guide other processes such as neural networks. Published in INFORMS Journal on Computing (ISSN 1091‑9856), formerly ORSA Journal on Computing (1989‑1995, ISSN 0899‑1499).

Abstract

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 short-term 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 II 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 II 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 of neural networks. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.