Artificial Bee Colony (ABC)
ABC models honey-bee foraging, first described by Dervis Karaboga (2005). Three roles divide the explore/exploit labor: employed bees exploit known food sources, onlooker bees pick sources by advertised quality (the “dance”), and scout bees abandon exhausted sources to explore new areas. Source: andrey-dik‘s MQL5 implementation (plus a modified “swarm” variant using fixed-size bee clusters per area rather than dynamic allocation).
How it works
Employed bees search near current food sources; onlookers probabilistically concentrate on the higher-fitness ones; when a source stops improving it is abandoned and a scout reseeds randomly — an explicit abandonment-driven exploration-vs-exploitation balance.
Benchmark (context-relative)
In this article’s early scoring, ABC ranked 4th, scoring 0.49447 (classic) / 0.50836 (modified). Its signature is “phenomenal convergence for smooth and discrete functions with few variables” — 100% convergence on all five 2-variable tests — but “average scalability,” decaying on 40- and 1000-param functions, with high parameter sensitivity and local-optima entrapment. Earlier, small-field methodology, not the mature %-of-MAX rating (see population-optimization-benchmark; no-free-lunch-theorem).
Related
metaheuristic-optimization · exploration-vs-exploitation · population-optimization-benchmark · no-free-lunch-theorem · andrey-dik · grey-wolf-optimizer · ant-colony-optimization