Recurrent neural network-predictions for PSO in dynamic optimization

Abstract

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.