Fine-tuning the Ant Colony System algorithm through Particle Swarm Optimization
This work addresses parameter tuning for a specific optimization algorithm, which is an incremental improvement for researchers in metaheuristics.
The authors tackled the problem of finding optimal parameters for the Ant Colony System algorithm on the Traveling Salesman Problem by using Particle Swarm Optimization to fine-tune them, achieving good quality solutions for single instances but with high computational times.
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.