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Social Interaction Investigation in Particle Swarm Optimization by Kenneth Lee

This project provides an overview of Particle Swarm Optimization (PSO) focusing on its history, implementation, processes, and expected results. It explores the social interactions among agent particles in PSO and studies various benchmark functions. The text also discusses the challenges faced, such as convergence issues and insufficient social interactions in Fully Informed Particle Swarm (FIPS).

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Social Interaction Investigation in Particle Swarm Optimization by Kenneth Lee

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  1. Investigation of Social Interaction Between Agent Particles by: Kenneth Lee Particle Swarm Optimization

  2. History Implementation Process Expected Results My Project An Overview of PSO • History • Implementation • Processes • Expected Results • My Project

  3. History Implementation Process Expected Results My Project History of PSO • Model of Flocks of Birds • Early Inefficiencies • Velocity Matching • Craziness • Refinement http://www.richard-seaman.com

  4. History Implementation Process Expected Results My Project Implementations of PSO • Used to find Function Minimums • N-Dimensional • Infinite search spaces • Neural Networking

  5. History Implementation Process Expected Results My Project Process of PSO • Initialize Swarm • Move Swarm • Calculate pbest and gbest • Adjust Velocities • Cognitive • Social • Convergence

  6. History Implementation Process Expected Results My Project Expected Results of PSO • Convergence • Correctness • Cheap • Memory • Time

  7. History Implementation Process Expected Results My Project My Project • Social Interactions • How do the particle talk to each other? • Can Different ways of talking improve performance? • Cost?

  8. History Implementation Process Expected Results My Project My Project – Current State Rastrigin Cornfield Six Camel Hump Current Benchmark Functions

  9. History Implementation Process Expected Results My Project My Project – Problems • FIPS (Fully Informed Particle Swarm)‏ • Converges on (0,0) instead of the minimum values • Not enough social interactions or benchmark testing functions

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