Tabu Search (TS) and, more recently, Scatter Search (SS) have proved highly effective in solving a wide range of optimization problems, and have had a variety of applications in industry, science, and government. The goal of Metaheuristic Optimization via Memory and Evolution: Tabu Search and Scatter Search is to report original research on algorithms and applications of tabu search, scatter search or both, as well as variations and extensions having "adaptive memory programming" as a primary focus. Individual chapters identify useful new implementations or new ways to integrate and apply the principles of TS and SS, or that prove new theoretical results, or describe the successful application of these methods to real world problems.

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Tabu Search (TS) and, more recently, Scatter Search (SS) have proved highly effective in solving a wide range of optimization problems, and have had a variety of applications in industry, science, and government. The goal of METAHEURISTIC OPTIMIZATION VIA MEMORY AND EVOLUTION: Tabu Search and Scatter Search is to report original research on algorithms and applications of tabu search, scatter search or both, as well as variations and extensions having "adaptive memory programming" as a primary focus. Individual chapters identify useful new implementations or new ways to integrate and apply the principles of TS and SS, or that prove new theoretical results, or describe the successful application of these methods to real world problems.

From the preface:

…Where Are We Headed?

The chapters of this book provide a series of landmarks along the way as we investigate and seek to better understand the elements of tabu search and scatter search that account for their successes in an astonishingly varied range of applications. The contributions of the chapters are diverse in scope, and are not uniform in the degree that they plumb or take advantage of fundamental principles underlying TS and SS. Collectively, however, they offer a useful glimpse of issues that deserve to be set in sharper perspective, and that move us farther along the way toward dealing with problems whose size and complexity pose key challenges to the optimization methods of tomorrow...

Fred Glover

University of Colorado

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