文档介绍：A New Ant Colony Algorithm Using the
Heterarchical Concept Aimed at Optimization of
Multiminima Continuous Functions
Johann Dr´eo and Patrick Siarry
Universit´e de Paris XII Val-de-Marne, Laboratoire d’Etude´ et de Recherche en
Instrumentation Signaux et Syst`emes (.), 61 avenue du G´en´eral de Gaulle,
94010 Cr´eteil, France
******@univ-, ******@univ-
Abstract. Ant colony algorithms are a class of metaheuristics which are
inspired from the behaviour of real ants. The original idea consisted in
simulating the munication, therefore these algorithms are
considered as a form of adaptive memory programming. A new formal-
ization is proposed for the design of ant colony algorithms, introducing
the biological notions of heterarchy munication channels. We are
interested in the way ant colonies handle the information. According to
these issues, an heterarchical algorithm called “Continuous Interacting
Ant Colony”(CIAC) is designed for the optimization of multiminima
continuous functions. CIAC uses munication channels showing
the properties of stigmergic and munications. CIAC presents
interesting emergent properties as it was shown through some analytical
test functions.
1 Introduction
Having recently given raise to a new metaheuristic method, the ant colony
metaphor proved to be a essful approach to solve “difficult” optimization
problems. The first algorithm inspired from the ant colony functioning is the
“ant system”(Colorni & al. 1991), which has been applied to bina-
torial problems. Until now, there are few adaptations of such algorithms to
continuous optimization problems. The first algorithm designed for continuous
function optimization was CACO (for Continuous Ant Colony Optimization)
(Bilchev & al. 1995) prises two levels: global and local. CACO uses
the ant colony framework to perform local searches, whereas global search is
handled by a ic algorithm. Indeed, the “global” ants perform a simpl