Learning-assisted Search Path Reconstruction Empowers Evolution Algorithm for Optimization

Evolutionary algorithms serve as a pivotal tool in addressing black-box problems, finding widespread applications across diverse academic disciplines and engineering domains. Despite their utility, these algorithms often confront challenges when navigating complex search spaces, impeding a comprehensive exploration of potential solutions. Solely depending on the algorithm’s exploration abilities falls short of fully harnessing the rich information contained within search spaces. To unlock the full potential of solution spaces, we introduce a deep learning method for the reconstruction of the search path. Specifically, discrete data sampled by evolutionary operators during the exploration process are collected, and a uniquely designed fully connected neural network is employed to reconstruct the exploration paths. The neural network’s robust fitting capability facilitates the transformation of initially discrete sampled information into a continuous form. By capitalizing on the reconstructed solution space information, the algorithm excels in identifying superior solutions. We refer to this method of deep learning-based search path reconstruction evolution strategy algorithm (DLES). The effectiveness of DLES is validated across multiple datasets, including CEC 2014, CEC 2018, CEC 2022 and BBOB. Experimental results, compared to several state-of-the-art algorithms, affirm the superiority of the DLES algorithm.