文档介绍：Physics of Life Reviews 2 (2005) 353–373
ate/plrev
Review
Ant colony optimization: Introduction and recent trends
Christian Blum 1
, LSI, Universitat ica de Catalunya, Jordi Girona 1-3, Campus Nord, 08034 Barcelona, Spain
Accepted 11 October 2005
Communicated by L. Perlovsky
Abstract
Ant colony optimization is a technique for optimization that was introduced in the early 1990’s. The inspiring source of ant
colony optimization is the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search
of approximate solutions to discrete optimization problems, to continuous optimization problems, and to important problems in
munications, such as routing and load balancing. First, we deal with the biological inspiration of ant colony optimization
algorithms. We show how this biological inspiration can be transfered into an algorithm for discrete optimization. Then, we outline
ant colony optimization in more general terms in the context of discrete optimization, and present some of the nowadays best-
performing ant colony optimization variants. After summarizing some important theoretical results, we demonstrate how ant colony
optimization can be applied to continuous optimization problems. Finally, we provide examples of an interesting recent research
direction: The hybridization with more classical techniques from artificial intelligence and operations research.
2005 Elsevier . All rights reserved.
Keywords: Ant colony optimization; Discrete optimization; Hybridization
Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354
2. The origins of ant colony optimization . . . . . . . . . ...............................................355
. Ant System for the TSP: The first ACO algorithm . ..........................................357
3. The ant colony optimization metaheurist