Adaptive Hybrid PSO with Q-Learning-Based Algorithm Selection.

Swarm-based algorithms are powerful in solving diverse optimisation problems. However it is still a challenging task for any single metaheuristic optimisation algorithm to perform optimally across all benchmark landscapes. As an example, while Particle Swarm Optimisation (PSO) often converges quickly on smooth unimodal functions, it may suffer from premature convergence on multimodal landscapes with many deceptive local optima. Therefore, this research aims to propose an adaptive hybrid optimisation algorithm in which a Q-Learning method selects complementary optimisation algorithms. Specifically, the proposed model uses a PSO warm-start phase followed by a tabular Q-Learning controller that dynamically selects between Firefly (FA) and Genetic Algorithm (GA). Algorithm selection is determined by population-level characteristics such as normalised diversity and normalised fitness spread. This RL-based search action controller is trained offline to learn an interpretable algorithm-selection policy and is applied with a dynamic Q-table during evaluation. Evaluated using various continuous and complex benchmark functions over multiple independent runs, the proposed algorithm outperforms standalone PSO, GA, FA and Simulated Annealing across diverse landscapes. Statistical validation using the Wilcoxon rank-sum test confirms significant improvements in most comparisons.