Multi-Agent Distributed Framework for Swarm Intelligence

Abstract This paper presents a multi-agent distributed framework for Swarm Intelligence (SI) based on our previous work ACODA (Ant Colony Optimization on a Distributed Architecture). Our framework can be used to distribute SI algorithms for solving graph search problems on a computer network. Examples and experimental results are given for SI algorithms of: Ant Colony System (ACS) and Bee Colony Optimization (BCO). In order to use the framework, the SI algorithms must be conceptualized to take advantage of the inherent parallelism determined by their analogy with natural phenomena (biological, chemical, physical, etc.): (i) the physical environment of the swarm entities is represented as a distributed multi-agent system and (ii) entities’ movement in the physical environment is represented as messages exchanged asynchronously between the agents of the problem environment. We present initial experimental results that show that our framework is scalable. We then compare the results of the distributed implementations of BCO and ACS algorithms using our framework. The conclusion was that our approach scales better when implementing the ACS algorithm but is faster when implementing BCO.

[1]  Douglas Thain,et al.  Distributed computing in practice: the Condor experience , 2005, Concurr. Pract. Exp..

[2]  Richard F. Hartl,et al.  A survey on pickup and delivery problems , 2008 .

[3]  Richard F. Hartl,et al.  A survey on pickup and delivery problems , 2008 .

[4]  Martín Pedemonte,et al.  A cellular ant colony optimisation for the generalised Steiner problem , 2010 .

[5]  Nicola Santoro,et al.  Design and analysis of distributed algorithms , 2006, Wiley series on parallel and distributed computing.

[6]  T. Seeley,et al.  The use of waggle dance information by honey bees throughout their foraging careers , 2005, Behavioral Ecology and Sociobiology.

[7]  Ronald L. Rivest,et al.  Introduction to Algorithms, third edition , 2009 .

[8]  Agostino Poggi,et al.  Developing Multi-agent Systems with JADE , 2007, ATAL.

[9]  Thomas Stützle,et al.  Stochastic Local Search: Foundations & Applications , 2004 .

[10]  Hai-Ying Sun,et al.  Parallel implementation of ant colony optimization on MPP , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[11]  Milan Tuba,et al.  Different approaches in parallelization of the artificial bee colony algorithm , 2011 .

[12]  S. A. Subbotin,et al.  Multiagent optimization based on the bee-colony method , 2009 .

[13]  Martín Pedemonte,et al.  A survey on parallel ant colony optimization , 2011, Appl. Soft Comput..

[14]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[15]  Costin Badica,et al.  Optimizing Communication Costs in ACODA Using Simulated Annealing: Initial Experiments , 2012, ICCCI.

[16]  Danny Weyns,et al.  Patterns of Delegate MAS , 2009, 2009 Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems.

[17]  Yue Zhang,et al.  BeeHive: An Efficient Fault-Tolerant Routing Algorithm Inspired by Honey Bee Behavior , 2004, ANTS Workshop.

[18]  F. Dyer.  The biology of the dance language. , 2002, Annual review of entomology.

[19]  Rafael Stubs Parpinelli,et al.  Parallel Approaches for the Artificial Bee Colony Algorithm , 2011 .

[20]  S. Priyanka,et al.  Parallel Ant-Miner (PAM) on High Performance Clusters , 2010, SEMCCO.

[21]  Costin Badica,et al.  Multi-agent approach to distributed ant colony optimization , 2013, Sci. Comput. Program..

[22]  Gerhard Reinelt,et al.  TSPLIB - A Traveling Salesman Problem Library , 1991, INFORMS J. Comput..

[23]  Tiranee Achalakul,et al.  Artificial bee colony algorithm on distributed environments , 2010, 2010 Second World Congress on Nature and Biologically Inspired Computing (NaBIC).

[24]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[25]  William J. Cook,et al.  The Traveling Salesman Problem: A Computational Study , 2007 .

[26]  Li-Pei Wong,et al.  Bee Colony Optimization with local search for traveling salesman problem , 2008, 2008 6th IEEE International Conference on Industrial Informatics.