A Learning-Based Particle Swarm Optimizer for Solving Mathematical Combinatorial Problems

This paper presents a set of adaptive parameter control methods through reinforcement learning for the particle swarm algorithm. The aim is to adjust the algorithm's parameters during the run, to provide the metaheuristics with the ability to learn and adapt dynamically to the problem and its context. The proposal integrates Q-Learning into the optimization algorithm for parameter control. The applied strategies include a shared Q-table, separate tables per parameter, and flexible state representation. The study was evaluated through various instances of the multidimensional knapsack problem belonging to the NP-hard class. It can be formulated as a mathematical combinatorial problem involving a set of items with multiple attributes or dimensions, aiming to maximize the total value or utility while respecting constraints on the total capacity or available resources. Experimental and statistical tests were carried out to compare the results obtained by each of these hybridizations, concluding that they can significantly improve the quality of the solutions found compared to the native version of the algorithm.

Automated summary

This paper presents adaptive parameter control methods utilizing reinforcement learning in the particle swarm algorithm. The integration of Q-learning into the optimization algorithm allows for parameter adjustments during the run, enabling the algorithm to dynamically learn and adapt to the problem and its context. Experimental evaluation using instances of the NP-hard multidimensional knapsack problem demonstrates significant improvement in solution quality compared to the native version of the algorithm.