A multi-swarm optimizer with a reinforcement learning mechanism for large-scale optimization

Large-scale global optimization (LSGO) problems involve numerous decision variables, are similar to real-world problems, and have generated research interest. To solve LSGO, a particle swarm optimizer (PSO) has been used. However, the many local optima and huge search space severely limit the effectiveness of the classic PSO. Dealing with the complexity of LSGO while avoiding the local optima is the main challenge of large-scale optimization algorithms. A multiswarm strategy has also been introduced to improve swarm diversity; however, it reduces the convergence speed. Previous studies have shown that reinforcement learning (RL) can improve the convergence ability of EAs owing to its increasing learning ability. In this study, we develop a multiswarm optimizer with an RL mechanism (MSORL) for LSGO. The MSORL includes a tri-particle group structure for subswarms to save the computational cost and balance the diversity and convergence. An RL-guided updating strategy is designed to enhance the convergence speed, and an adaptive tolerance-based search mechanism is employed to improve the diversity and avoid the local optima. The experimental results prove that the MSORL outperforms other state-of-the-art algorithms in terms of the convergence accuracy and speed.