Amaury Hernandez-Aguila
ABSTRACT
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.
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Index Terms
- Randomized parameter settings for a pool-based particle swarm optimization algorithm: a comparison between dynamic adaptation of parameters and randomized parameterization
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