My Favorite Algorithm: ACO

Tiny creatures with a massive impact.

One of my favorite little things about life is seeing a swarm of ants carry a small piece of food back to home base. On an individual level, ants are small and unimpressive, but together, they display a remarkable sight - finding the shortest path from a food source back to the colony. This collective behavior inspired - what I believe to be - the most fascinating algorithm in all of CS: Ant Colony Optimization (AC0).

At its core, ACO is about imitation. Ants leave behind pheremone trails as they move, and other ants tend to follow stronger trails. Over time, shorter paths accumulate snowball more pheremones, resulting in a colony convergence onto the optimal (or near-optimal) route.

In the early '90s, computer scientists took this idea and turned it into a powerful solution to combinatorial optimization problems, such as the Traveling Salesman Problem (finding the shortest route while visiting some list of cities) or optimizing network routing.

Here is the general metaheuristic structure of the algorithm:

func ACO:
  while not_terminated:
    generateSolutions():
    daemonActions():
    pheremoneUpdate():
  repeat
end func

generateSolutions() - each ant constructs a solution using pheromone trails & 
hueristic information such as distances

daemoneActions() - optional central operations, like applying a local search 
to improve solutions

pheromoneUpdate() - trails are updated, evaporation reduces the old pheromone,
reinforcement increases pheromone on edges used in OK solutions

The above ACO algorithm leads to:

• emergent intelligence
• adaptability
• parallelism
• simplistic elegance

My personal favorite application of ACO is in that of robotic swarm coordination; used to guide a group of simple robots to collectively perform tasks like path planning.

Written by me | August 2025