Long Short-Term Memory Network Assisted Evolutionary Algorithm for Computationally Expensive Multiobjective Optimization

Computationally expensive multiobjective optimization problems (EMOPs) that require significant computational resources are commonly encountered in realworld applications. To address the challenges associated with such problems, using computationally inexpensive surrogate models to approximate objectives has emerged as an effective approach to handle EMOPs. However, the current collaboration between evolutionary algorithms (EAs) and surrogate models is limited, relying on static regression or classification methods that do not fully capture the dynamic evolution process of EAs. This study aims to advance the integration of surrogateassisted multiobjective optimization by incorporating time-series prediction models. The target is to track the evolutionary trajectory of an EA and enhance its search capability. Specifically, long short-term memory (LSTM) networks are embedded into an EA for surrogateassisted optimization (SAO). The role of LSTM networks in SAO is thoroughly investigated through ablation studies. Experimental results on six EMOPs demonstrate the potential of using LSTM networks in SAO. The results are compared with those obtained from four representative surrogateassisted EAs, providing insights into the effectiveness of LSTM-based approaches in addressing EMOPs.