Particle Swarm Optimization
Particle Swarm Optimization
From Hand Calculations to AI-Assisted Code
You can already ask an AI to write you a Particle Swarm Optimizer. The problem is knowing whether the swarm is genuinely searching — or whether every particle stampeded toward the first decent solution and converged on it before exploring anything better. That behavior traces back to a handful of weights most people copy from a tutorial and never understand. This is the course that gives you that judgment.
Five parts, one algorithm, no gaps:
Part 1 — Intro. How a swarm of simple particles, each nudged by its own best memory and pulled toward the best the whole swarm has found, converges on good solutions to hard problems without a single derivative. The intuition before the notation.
Part 2 — By Hand. A complete worked example, iteration by iteration: update each particle's velocity from its personal best and the global best, move it, re-evaluate — every component and every number shown. Nothing waved away, nothing left "as an exercise." You prove the algorithm to yourself.
Part 3 — In Excel. The same example built out in a spreadsheet you can open and poke at, so you watch the swarm converge — and see for yourself how shifting the balance between personal pull, social pull, and inertia changes whether it explores or rushes in.
Part 4 — In Python. A clean, executable, verified reference implementation — the known-good version you can check any other code against.
Part 5 — With AI. A tested prompt that directs an AI assistant to carry this same algorithm to your problem, plus the understanding to catch it when it's wrong.
By the end you won't just have working code. You'll have followed the algorithm from first principles through a hand calculation, a spreadsheet, and production Python — so when you point an AI at your own optimization problem, you'll know how to balance the swarm's personal and social pulls against inertia on purpose, spot premature convergence before it costs you, and tell when the answer is right.
Written for practicing engineers and STEM professionals who'd rather understand a method than trust a black box.
