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Applying Aspects of Multi-robot Search to Particle Swarm Optimization

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Ant Colony Optimization and Swarm Intelligence (ANTS 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4150))

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.

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© 2006 Springer-Verlag Berlin Heidelberg

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Pugh, J., Segapelli, L., Martinoli, A. (2006). Applying Aspects of Multi-robot Search to Particle Swarm Optimization. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2006. Lecture Notes in Computer Science, vol 4150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11839088_54

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  • DOI: https://doi.org/10.1007/11839088_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38482-3

  • Online ISBN: 978-3-540-38483-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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