Nature-inspired Parameter Controllers for ACO-based Reactive Search

Abstract

This study proposes machine learning strategies to control the parameter adaptation in ant colony optimization algorithm, the prominent swarm intelligence metaheuristic. The sensitivity to parameters' selection is one of the main limitations within the swarm intelligence algorithms when solving combinatorial problems. These parameters are often tuned manually by algorithm experts to a set that seems to work well for the problem under study, a standard set from the literature or using off-line parameter tuning procedures. In the present study, the parameter search process is integrated within the running of the ant colony optimization without incurring an undue computational overhead. The proposed strategies were based on a novel nature-inspired idea. The results for the travelling salesman and quadratic assignment problems revealed that the use of the augmented strategies generally performs well against other parameter adaptation methods.