The Ant System Applied to the Quadratic Assignment Problem

by: Vittorio Maniezzo, Alberto Colorni

Knowledge and Data Engineering, Vol. 11, No. 5. (1999), pp. 769-778.

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Abstract

. Ant Colony Optimization (ACO) is a class of constructive metaheuristic algorithms sharing the common approach of constructing a solution on the basis of information provided both by a standard constructive heuristic and by previously constructed solutions. This tutorial is composed of three parts. The rst one frames the ACO approach in current trends of research on metaheuristic algorithms for combinatorial optimization; the second outlines current research within the ACO framework,...

@article{citeulike:2098446, abstract = {. Ant Colony Optimization (ACO) is a class of constructive metaheuristic algorithms sharing the common approach of constructing a solution on the basis of information provided both by a standard constructive heuristic and by previously constructed solutions. This tutorial is composed of three parts. The rst one frames the ACO approach in current trends of research on metaheuristic algorithms for combinatorial optimization; the second outlines current research within the ACO framework,...}, author = {Maniezzo, Vittorio and Colorni, Alberto }, citeulike-article-id = {2098446}, journal = {Knowledge and Data Engineering}, keywords = {ant}, number = {5}, pages = {769--778}, posted-at = {2007-12-12 12:37:20}, priority = {2}, title = {The Ant System Applied to the Quadratic Assignment Problem}, url = {http://citeseer.ist.psu.edu/197565.html}, volume = {11}, year = {1999} }

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TY - JOUR ID - citeulike:2098446 L3 - citeulike-article-id:2098446 TI - The Ant System Applied to the Quadratic Assignment Problem JF - Knowledge and Data Engineering VL - 11 IS - 5 SP - 769 EP - 778 N2 - . Ant Colony Optimization (ACO) is a class of constructive metaheuristic algorithms sharing the common approach of constructing a solution on the basis of information provided both by a standard constructive heuristic and by previously constructed solutions. This tutorial is composed of three parts. The rst one frames the ACO approach in current trends of research on metaheuristic algorithms for combinatorial optimization; the second outlines current research within the ACO framework,... KW - ant AU - Maniezzo, Vittorio AU - Colorni, Alberto PY - 1999/// UR - http://citeseer.ist.psu.edu/197565.html ER -

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