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 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 of neural networks. T a b u search is a strategy for solving combinatorial optimization problems whose applications range from graph theory and matroid settings to general pure and mixed integer programming problems. It is an adaptive procedure with the ability to make use of many other methods, such as linear programming algorithms and specialized heuristics, which it directs to overcome the limitations of local optimality. Tabu search has its origins in combinatorial procedures applied to nonlinear covering problems in the late 1970~,[~1 and subsequently applied to a diverse collection of problems ranging from scheduling and computer channel balancing to cluster analysis and space planning.'3.4,6.71 Latest research and computational comparisons involving traveling salesman, graph coloring, job shop flow sequencing, integrated circuit design and time tabling problems have likewise disclosed the ability of tabu search to obtain high quality solutions with modest computational effort, generally dominating alternative methods tested.['. 12-13. A recent independent development of several of its ideas[lol also has been applied successfully to maximum satisfiability problems.[''] Such applications, for problems ranging in size from hundreds to millions of variables, are elaborated in Section 10.
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