Applying aspects of multi-robot search to particle swarm optimization

Authors:
Jim Pugh;Loïc Segapelli;Alcherio Martinoli
Affiliations:
Swarm-Intelligent Systems Group, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland;Swarm-Intelligent Systems Group, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland;Swarm-Intelligent Systems Group, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Venue:
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Year:
2006

Citing 1
Cited 1

Multi-robot learning with particle swarm optimization

AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems

A PSO-based algorithm designed for a swarm of mobile robots

Structural and Multidisciplinary Optimization

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Abstract

Throughout the history of research, some of the most innovative and useful discoveries have arisen from a fusion of two or more seemingly unrelated fields of study; a characteristic of some method or process is enfused into a completely disjoint technique, and the resulting creation exhibits superior behavior. Some common examples include simulated annealing modeled after the annealing process in physics, Ant Colony Optimization modeled after the behavior of social insects, and the Particle Swarm Optimization algorithm modeled after the patterns of flocking birds.