Randomized parameter settings for a pool-based particle swarm optimization algorithm
Amaury Hernández-Águila, Mario García-Valdéz, Juan Julián Merelo, Oscar Castillo · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2017
This work makes a comparison between different parameter tuning strategies and a strategy based on randomized parameterization in a pool-based model for the particle swarm optimization algorithm. The proposed method is compared against strategies that implement dynamic adaptation of parameters through the use of fuzzy inference systems. The experiments show results that support a hypothesis stating that the use of randomized parameterization can make a pool-based particle swarm optimization algorithm perform as well as its dynamically adapted counterpart.