Recurrent neural network-predictions for PSO in dynamic optimization
Almuth Meier, Oliver Krämer · Proceedings of the Genetic and Evolutionary Computation Conference · 2018
In order to improve particle swarm optimization (PSO) to tackle dynamic optimization problems, various strategies have been introduced, e. g., random restart, memory, and multi-swarm approaches. However, literature lacks approaches based on prediction. In this paper we propose three different PSO variants employing a prediction approach based on recurrent neural networks to adapt the swarm behavior after a change of the objective function. We compare the variants in an experimental study to a PSO algorithm that is solely based on re-randomization. The experimental study comprises the moving peaks benchmark and dynamic extensions of the Sphere, Rastrigin, and Rosenbrock functions for showing the strengths of the prediction-based PSO variants regarding convergence.