4th International Conference on Intelligent and Fuzzy Systems (INFUS), Bornova, Turkey, 19 - 21 July 2022, vol.504, pp.839-846
In this work, we present a distributed platform to execute multi-population metaheuristics. As proof of concept, we present an implementation using two metaheuristics: Genetic Algorithms, and Particle Swarm Optimization. We execute these multi-population algorithms asynchronously using a queue-based architecture. We optimize the parameters defining the membership functions of a rear-wheel fuzzy controller. We compare the results with a non-distributed sequential alternative and show the benefits of mixing the algorithms' populations and integrating a migration process between them.