Fine-tuning the Ant Colony System algorithm through Particle Swarm Optimization

Ant Colony System (ACS) is a distributed (agent- based) algorithm which has been widely studied on the Symmetric Travelling Salesman Problem (TSP). The optimum parameters for this algorithm have to be found by trial and error. We use a Particle Swarm Optimization algorithm (PSO) to optimize the ACS parameters working in a designed subset of TSP instances. First goal is to perform the hybrid PSO-ACS algorithm on a single instance to find the optimum parameters and optimum solutions for the instance. Second goal is to analyze those sets of optimum parameters, in relation to instance characteristics. Computational results have shown good quality solutions for single instances though with high computational times, and that there may be sets of parameters that work optimally for a majority of instances.

💡 Research Summary

The paper addresses a well‑known drawback of the Ant Colony System (ACS) when applied to the symmetric Travelling Salesman Problem (TSP): the performance of ACS is highly sensitive to a set of algorithmic parameters (pheromone evaporation rate ρ, heuristic influence α, pheromone influence β, exploitation‑exploration balance q₀, initial pheromone τ₀, etc.). Traditionally these parameters are set by trial‑and‑error or simple grid searches, which is time‑consuming and often sub‑optimal for a given instance. To overcome this limitation, the authors propose a hybrid meta‑optimization framework that uses Particle Swarm Optimization (PSO) to automatically tune the ACS parameters.

Methodology
Each particle in the PSO swarm encodes a five‑dimensional real‑valued vector corresponding to the ACS parameters. The search space is bounded by ranges derived from prior literature (e.g., α∈