Integrating Particle Swarm Optimization with Reinforcement Learning: A Promising Approach to Optimization

The popular optimization method, Particle Swarm Optimization (PSO), drew inspiration from the collective behavior of fish schools and bird flocks, leading to its development. PSO has some drawbacks as it exhibits slow convergence for complex optimization problems and tends to converge prematurely in local optima. PSO was primarily designed for single-objective optimization problems. Nevertheless, numerous real-life challenges encompass multiple objectives. In contrast, reinforcement learning (RL) is a semi-supervised learning framework where an intelligent agent interacts with its environment, making rational decisions to maximize the associated reward. RL, however, can be computationally intensive. In this chapter, we investigate the recent research work to overcome the aforementioned issues by integrating RL with PSO. This approach has garnered substantial attention in recent years. In this approach, PSO is used to explore the search space and find potential solutions, while RL is used to refine and optimize the solutions found by PSO. We also discuss the methodology used in various research works in hybrid RL and identify directions for future research. Our analysis underscores RL’s potential as a valuable instrument for enhancing the efficiency and effectiveness of PSO algorithms.