Journal of System Simulation ›› 2026, Vol. 38 ›› Issue (9): 2574-2589.doi: 10.16182/j.issn1004731x.joss.25-1017

• Papers • Previous Articles    

High-dimensional Multi-objective Optimization Algorithm Based on Large Language Model Assistance

Ma Yichao, Wei Jingxuan   

  1. School of Computer Science and Technology, Xidian University, Xi'an 710126, China
  • Received:2025-10-22 Revised:2025-11-18 Online:2026-09-30 Published:2026-10-02
  • Contact: Wei Jingxuan

Abstract:

The increase in the dimension of objective functions in optimization problems makes it challenging to balance convergence and diversity in population-based algorithms, which is a key factor leading to performance degradation of multi-objective evolutionary algorithms. To address this issue, a dynamic switching mechanism assisted by a large language model is proposed. By continuously evaluating the changes in hypervolume and convergence metrics of the solution set, the search process is adaptively divided into three phases: convergence-oriented, diversity-oriented, and large language model-suggested phases, achieving precise allocation of computational resources. Furthermore, to mitigate the high computational cost of large language models in high-dimensional scenarios, a low-cost invocation strategy is designed: the model is invoked only when performance declines for several consecutive generations, thereby guiding the population toward more efficient search. Comparative experiments with six state-of-the-art algorithms are conducted on 5 to 15-objective test sets from the DTLZ and WFG benchmark problems. The results demonstrate that the proposed method exhibits superior performance in both convergence and diversity metrics, confirming the effectiveness of large language models in assisting evolutionary algorithms to handle complex high-dimensional optimization problems.

Key words: high-dimensional multi-objective optimization, evolutionary algorithm, co-evolution, dynamic switching mechanism, large language model

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