Ant colony optimization and its application to adaptive routing in telecommunication networks

Abstract

In ant societies, and, more in general, in insect societies, the activities of the individuals, as well asofthesocietyasawhole,arenotregulatedbyanyexplicit
\nformofcentralizedcontrol. Onthe other hand, adaptive and robust behaviors transcending the behavioral repertoire of the single individualcanbeeasilyobserved at society level. Thesecomplexglobalbehaviorsaretheresult of self-organizing dynamics driven by local interactions and communications among a number of relatively simple individuals. The simultaneous presence of these and other fascinating and unique characteristics have made ant societies an attractive and inspiring model for building newalgorithmsandnewmulti-agentsystems. Inthelastdecade,antsocietieshavebeentakenasa referenceforanevergrowingbodyof scientific work, mostly in the fields of robotics, operations research, and telecommunications. Among the different works inspired by ant colonies, the Ant Colony Optimization metaheuristic (ACO) is probably the most successful and popular one. The ACO metaheuristic is a multi-agent framework for combinatorial optimization whose main components are: a set of ant-like agents, the use of memory and of stochastic decisions, and strategies of collective and distributed learning. It finds its roots