Reinforcement Learning Based on Population-Based Incremental Learning for the Self-Adaptive Dhole Optimization Algorithm

Adaptive metaheuristics are optimization algorithms that dynamically adjust their search behavior to improve performance during an optimization run. Adaptive strategies are among the techniques developed to improve the search performance of metaheuristics. This study proposes a Reinforcement Learning-based Dhole Optimization Algorithm with Population-Based Incremental Learning (RL-DOA-PBIL) to improve search performance in real-world constrained optimization problems. The proposed adaptive strategy is based on a probabilistic learning mechanism that guides search dynamics through self-adjusted learning and mutation parameters. By embedding distribution-guided learning into the dhole-hunting mechanism, RL-DOA-PBIL strengthens search diversity in the early stages while promoting more refined convergence toward promising feasible regions. The performance of RL-DOA-PBIL is validated on the IEEE Congress on Evolutionary Computation (CEC2020) benchmark suite for real-world constrained optimization, comprising 57 challenging problems across six engineering domains. For fair assessment, 25 independent runs of all algorithms are executed with the same maximum number of function evaluations. Statistical results, including mean fitness values, standard deviations, and Friedman ranks, are collected to measure algorithm performance. The obtained results demonstrate that RL-DOA-PBIL achieves the best overall Friedman rank of 2.41, outperforming its predecessor, Dhole Optimization Algorithm (DOA), and all other competitors across most benchmark categories. These findings indicate that the proposed algorithm can serve as an effective optimization tool for solving complex constrained engineering design problems, supporting improved decision-making, resource utilization, and system performance in real-world applications.