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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 —(More)
Scope and Purpose-A summary is provided of some of the recent (and a few not-so-recent) developments that otTer promise for enhancing our ability to solve combinatorial optimization problems. These developments may be usefully viewed as a synthesis of the perspectives of operations research and artificial intelligence. Although compatible with the use of(More)
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(More)
Scatter search and its generalized form called path relinking are evolutionary methods that have recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, these methods use strategies for combining(More)
In this paper we consider the NP-hard problem of finding a feasible solution (if any exists) for a generic MIP problem of the form min{c T x : Ax ≥ b, x j integer ∀j ∈ I}. Trivially, a feasible solution can be defined as a point x * ∈ P := {x : Ax ≥ b} that is equal to its rounding x, where the rounded point x is defined by x j := [x * j ] if j ∈ I and x j(More)
This is the second half of a two part series devoted to the tabu search metastrategy for optimization problems. Part I introduced the fundamental ideas of tabu search as an approach for guiding other heuristics to overcome the limitations of local optimality, both in a deterministic and a probabilistic framework. Part I also reported successful applications(More)
We describe the main features of tabu search, emphasizing a perspective for guiding a user to understand basic implementation principles for solving combinatorial or nonlinear problems. We also identify recent developments and extensions lhat have contributed to increasing the efficiency of the method. One of the useful aspects of tabu search is the ability(More)
We present a linearization strategy for mixed 0-1 quadratic programs that produces small formulations with tight relaxations. It combines constructs from a classical method of Glover and a more recent reformulation-linearization technique (RLT). By using binary identities to rewrite the objective, a variant of the first method results in a concise(More)