Iterated Tabu Search for the Unconstrained Binary Quadratic Optimization Problem
Volume 17, Issue 2 (2006), pp. 279–296
Pub. online: 1 January 2006
Type: Research Article
Received
1 January 2005
1 January 2005
Published
1 January 2006
1 January 2006
Abstract
Given a set of objects with profits (any, even negative, numbers) assigned not only to separate objects but also to pairs of them, the unconstrained binary quadratic optimization problem consists in finding a subset of objects for which the overall profit is maximized. In this paper, an iterated tabu search algorithm for solving this problem is proposed. Computational results for problem instances of size up to 7000 variables (objects) are reported and comparisons with other up-to-date heuristic methods are provided.