Artificial Bee Colony
Artificial Bee Colony
From Hand Calculations to AI-Assisted Code
You can already ask an AI to write you an Artificial Bee Colony optimizer. The problem is knowing whether its scouts are actually doing their job — or whether the colony is clinging to played-out solutions long after they've stopped improving, or abandoning promising ones before they've paid off. The whole method balances on when a food source gets given up as exhausted, and it's the part a copy-paste implementation quietly gets wrong. This is the course that gives you that judgment.
Five parts, one algorithm, no gaps:
Part 1 — Intro. How a colony split into three roles — employed bees exploiting known food sources, onlookers reinforcing the best of them, and scouts abandoning exhausted ones to search anew — balances digging deeper against looking elsewhere, all without a single derivative. The intuition before the notation.
Part 2 — By Hand. A complete worked example, cycle by cycle: send the employed bees out, share the results, let the onlookers choose where to concentrate, and trigger a scout when a source runs dry — every candidate 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 colony converge — and see for yourself how changing the point at which a source is abandoned tips the search between stubborn exploitation and restless exploration.
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 set the abandonment threshold on purpose, balance the colony's exploiting against its exploring, 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.
