TABU search

This article explores the meta-heuristic approach called tabu search, which is dramatically changing our ability to solve a host of problems in applied science, business and engineering. Tabu search has important links to evolutionary and “genetic” methods, often overlooked, through its intimate connection with scatter search and path relinking — evolutionary procedures that have recently attracted attention for their ability to facilitate the solution of complex problems. The adaptive memory designs of tabu search have also provided useful alternatives and supplements to the types of memory embodied in neural networks, allowing enhancements of neural network processes in practical settings. 1. Tabu Search Background and Relevance Faced with the challenge of solving hard optimization problems that abound in the real world, classical methods often encounter great difficulty. Vitally important applications in business, engineering, economics and science cannot be tackled with any reasonable hope of success, within practical time horizons, by solution methods that have been the predominant focus of academic research throughout the past three decades (and which are still the focus of many textbooks). The meta-heuristic approach called tabu search (TS) is dramatically changing our ability to solve problems of practical significance. Current applications of TS span the realms of resource planning, telecommunications, VLSI design, financial analysis, scheduling, space planning, energy distribution, molecular engineering, logistics, pattern classification, flexible manufacturing, waste management, mineral exploration, biomedical analysis, environmental conservation and scores of others. In recent years, journals in a wide variety of fields have published tutorial articles and computational studies documenting successes by tabu search in extending the frontier of problems that can be handled effectively — yielding solutions whose quality often significantly surpasses that obtained by methods previously applied. Table 1.1 gives a partial catalog of example applications. A more comprehensive list, including summary descriptions of gains achieved from practical implementations, can be found in Glover and Laguna, 1997. Reports of recent TS implementations can also be found on the web page http://www.upt.pt/tabusearch. A distinguishing feature of tabu search is embodied in its exploitation of adaptive forms of memory, which equips it to penetrate complexities that often confound alternative approaches. Yet we are only beginning to tap the rich potential of adaptive memory strategies, and the discoveries that lie ahead promise to be as important and exciting as those made to date. The knowledge and principles that have emerged from the TS framework give a foundation to create practical systems whose capabilities markedly exceed those available earlier. At the same time, there are many untried variations that may lead to further advances. A conspicuous feature of tabu search is that it is dynamically growing and evolving, drawing on important contributions