Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Wasp-like Agents for Distributed Factory Coordination
Autonomous Agents and Multi-Agent Systems
Analysis of Dynamic Task Allocation in Multi-Robot Systems
International Journal of Robotics Research
Emergent Topology Control Based on Division of Labour in Ants
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 01
Ant colony intelligence in multi-agent dynamic manufacturing scheduling
Engineering Applications of Artificial Intelligence
Auction-based multi-robot task allocation in COMSTAR
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
The Influence of Memory in a Threshold Model for Distributed Task Assignment
SASO '08 Proceedings of the 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Task allocation via self-organizing swarm coalitions in distributed mobile sensor network
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
On model design for simulation of collective intelligence
Information Sciences: an International Journal
Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions
Information Sciences: an International Journal
Information Sciences: an International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Distributed estimation over complex networks
Information Sciences: an International Journal
Information Sciences: an International Journal
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Multi-agent algorithms inspired by the division of labour in social insects and by markets, are applied to a constrained problem of distributed task allocation. The efficiency (average number of tasks performed), the flexibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved efficiency and robustness. We employ nature inspired particle swarm optimisation to obtain optimised parameters for all algorithms in a range of representative environments. Although results are obtained for large population sizes to avoid finite size effects, the influence of population size on the performance is also analysed. From a theoretical point of view, we analyse the causes of efficiency loss, derive theoretical upper bounds for the efficiency, and compare these with the experimental results.