Academic Updates: Jing Wang from School of Software and Internet of Things Engineering Published Paper on Top SCI Journal ELSEVIER

Recently, Jing Wang, a young teacher from the School of Software and Internet of Things Engineering, published a research paper titled "A novel multi-state reinforcement learning-based multi-objective evolutionary algorithm" in the internationally renowned journal ELSEVIER. The paper introduces a multi-objective evolutionary algorithm (MRL-MOEA) based on multi-state reinforcement learning, which combines the decision-making capabilities of reinforcement learning with the demands of multi-objective optimization, significantly enhancing the performance of the algorithm.

The main work of the research includes three aspects: First, by constructing a state model based on the distribution of individuals in the objective space and using a reinforcement learning framework to dynamically select the optimal crossover operator, a balance between diversity and convergence is achieved. Second, in response to the insufficient convergence in certain areas of the Pareto front, a strategy for adjusting weight vectors has been proposed to improve the distribution in sparse areas and achieve a more uniform Pareto front; Finally, the performance of MRL-MOEA was widely verified on multiple benchmark test sets including WFG and DTLZ, proving the algorithm's excellent solving ability and competitive advantage when dealing with problems involving 3 to 10 objectives. The experimental results fully prove the practicality and superiority of the algorithm in complex multi-objective optimization.

Jing Wang's research achievement has not only attracted widespread attention in the academic community, but also provided new research ideas and methods for the field of multi-objective optimization, which has important theoretical and practical application value.

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