Human Learning Optimization Improved Based on Pre-Trained Language Models for Global Optimization and Educational Course Grade Prediction

The Human Learning Optimization (HLO) algorithm is a binary metaheuristic algorithm that simulates human random learning, individual learning, and social learning processes. Owing to its simple structure and low computational cost, HLO has been widely applied to continuous optimization and machine-learning hyperparameter tuning. However, when applied to continuous optimization, the original HLO suffers from three major limitations: the Hamming cliff phenomenon associated with standard binary encoding, the limited source of knowledge for social learning, and an almost fixed learning probability. To address these issues, this study proposes a Gray-code and successful-memory human learning optimization algorithm based on a pretrained language model, termed LGSM-HLO. Specifically, the Pre-trained Language Model-Inspired Strategy (PLMS) treats elite encodings as a knowledge corpus and extracts shared encoding patterns through weighted pretraining, masked reconstruction, and online fine-tuning. The Gray-code Coarse-to-Fine Semantic Learning strategy (GCFSL) employs Gray coding to improve encoding-neighborhood consistency and dynamically adjusts the learning granularity throughout the evolutionary process. The Successful-History Differential Teacher Strategy (SHDTS) integrates historical successful parameters, high-quality individuals, and population differential information to construct personalized social teachers, while providing a Cauchy-based escape mechanism for stagnated individuals. Experiments on the CEC2017 (30-dimensional) and CEC2022 (10- and 20-dimensional) benchmark suites show that LGSM-HLO achieves superior mean performance and standard deviation on most functions compared with nine competitive algorithms. The Wilcoxon rank-sum test and Friedman rank test further confirm its overall superiority. Moreover, none of the three proposed strategies introduces additional objective-function evaluations, and the proposed algorithm retains the same dominant asymptotic computational complexity as the original HLO. Furthermore, an LGSM-HLO-RF model is developed by employing LGSM-HLO for joint hyperparameter optimization of a random forest. In the three-class prediction of higher-education student achievement, LGSM-HLO-RF outperforms the comparison algorithms in terms of Accuracy, Macro-F1, and Macro-Recall, demonstrating the strong transferability of LGSM-HLO from benchmark optimization to practical educational data-mining tasks.