A decomposition-based multi-objective evolutionary algorithm with Q-learning for adaptive operator selection

In the past few decades, many multi-objective evolution algorithms (MOEAs) have been proposed, often emphasizing a single crossover operator, which has a significant impact on the algorithm’s performance. This paper proposed a novel MOEA, based on the MOEA/D framework and employing Q-learning for adaptive operator selection (QLMOEA/D-AOS). In every Iteration, Q-learning is used to dynamically choose an operator among five crossover operators. To obtain a better distribution of solutions in multi-objective optimization problems with irregular PFs, a new approach for weight vector initializing is proposed. Additionally, to enhance population diversity, a reward calculation method based on two metrics, Spacing and PD, is proposed. Finally, the proposed algorithm is validated for different numbers of objectives, ranging from two to five for multi/many-objective optimization problems. The experimental results demonstrate the significant advantages of the proposed algorithm compared to state-of-the-art MOEAs across multiple test cases.