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Module 13: Particle Swarm Optimization - Flocking Toward the Optimum

Inspired by bird flocking and fish schooling, PSO moves a swarm of particles through the search space, each influenced by its own best position and the swarm's best. Simple, fast to implement, and surprisingly competitive.

Learning Objectives​

  1. Implement PSO from scratch with velocity update and position update
  2. Understand inertia weight and constriction factor
  3. Code different topologies (global best, ring, von Neumann)
  4. Implement binary PSO for discrete problems
  5. Use pyswarms for production PSO

Concept Explanation​

Coming soon.

Code Examples​

Coming soon.

Exercises​

Coming soon.

Milestone Checklist​

  • Built PSO from scratch
  • Understand inertia weight vs constriction
  • Implemented ring topology
  • Can compare PSO vs CMA-ES on continuous problems

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