Swarm Optimization Using Agents Modeled as Distributions
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Particle Swarm Optimization (PSO) is a popular meta-heuristic for black-box optimization. Many variations and extensions of PSO have been developed since its creation in 1995, and the algorithm remains a popular topic of research. In this work we explore a new, abstracted, perspective of the PSO system and present the novel Particle Field Optimization (PFO) algorithm which harnesses this new perspective to achieve a behaviour distinct from traditional PSO systems.
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Copyright © 2014 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2014
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