Title
A Nonlinear Multi-agent System designed for Swarm Intelligence: the Logistic MAS
Abstract
Ant algorithms and flocking algorithms are the two main programming paradigms in swarm intelligence. They are built on stochastic models, widely used in optimization problems. However, though this modeling leads to high- performance algorithms, some mechanisms, like the symmetry break in ant decision, are still not well understood at the local ant level. Moreover, there is currently no modeling approach which joins the two paradigms. This paper proposes an entirely novel approach to the mathematical foundations of swarm algorithms: contrary to the current stochastic approaches, we show that an alternative deterministic model exists, which has its origin in deterministic chaos theory. We establish a reactive multi-agent system, based on logistic nonlinear decision maps, and designed according to the influence-reaction scheme. The rewriting of the decision functions leads to a new way of understanding the swarm phenomena in terms of state synchronization, and enables the analysis of their convergence behavior through bifurcation diagrams. We apply our approach on two concrete examples of each algorithm class, in order to demonstrate its general applicability.
Year
DOI
Venue
2007
10.1109/SASO.2007.1
Cambridge, MA
Keywords
Field
DocType
logistic mas,swarm algorithm,nonlinear multi-agent system,ant decision,ant algorithm,swarm intelligence,modeling approach,logistic nonlinear decision map,novel approach,current stochastic approach,decision function,local ant level,bifurcation diagram,programming paradigm,bifurcation,logistics,artificial intelligence,stochastic processes,ant colony optimization,flocking algorithm,behavior modeling,particle swarm optimization,symmetry breaking,multi agent systems,concrete,multiagent systems,multi agent system,stochastic model
Mathematical optimization,Programming paradigm,Swarm behaviour,Computer science,Swarm intelligence,Flocking (behavior),Multi-agent system,Artificial intelligence,Deterministic system,Optimization problem,Chaos theory
Conference
ISBN
Citations 
PageRank 
0-7695-2906-2
5
0.59
References 
Authors
11
3
Name
Order
Citations
PageRank
Rodolphe Charrier1162.42
Christine Bourjot210213.97
Francois Charpillet315416.96