Grigorios Piperagkas

Integrating Particle Swarm Optimization with Reinforcement Learning in Noisy Problems

Title: Integrating Particle Swarm Optimization with Reinforcement Learning in Noisy Problems

Noisy optimization problems arise very often in real–life applications. A common practice to tackle problems characterized by uncertainties, is the re–evaluation of the objective function at every point of interest for a fixed number of replications. The obtained objective values are then averaged and their mean is considered as the approximation of the actual objective value. However, this approach can prove inefficient, allocating replications to unpromising candidate solutions. We propose a hybrid approach that integrates the established Particle Swarm Optimization algorithm with the Reinforcement Learning approach to efficiently tackle noisy problems by intelligently allocating the available computational budget. Two variants of the proposed approach, based on different selection schemes, are assessed and compared against the typical alternative of equal sampling. The results are reported and analyzed, offering significant evidence regarding the potential of the proposed approach.