# Automated Generation of Hybrid Metaheuristics Using Learning-to-Rank
## 1 Introduction
In addition, [PERSON] [13] introduced an RL-based metaheuristic (RLWOA) that leverages a deep Q-learning agent to adjust WOA parameters online, resulting in marked improvements in convergence rate and solution quality on a suite of global optimization problems. Drawing on these insights, developing metaheuristic algorithms can be regarded as tackling a combinatorial optimization task, with the solution space including factors such as algorithm parameters, algorithm composition, and algorithmic components. Related research can be divided into three categories: automated algorithm configuration, selection, and composition [1]. Algorithm configuration focuses on optimizing parameters for specific algorithms, while algorithm selection involves choosing algorithms based on problem characteristics. Although both configuration and selection offer distinct advantages, they require a certain degree of prior knowledge or accurate identification of problem features. In contrast, algorithm composition provides greater flexibility by generating new algorithms through the combination of basic components, thereby eliminating dependence on original algorithm structures. Furthermore, Learning-to-Rank (LTR) techniques, owing to their lightweight nature and strong interpretability, have demonstrated significant potential in automated algorithm composition and generation. LTR can directly model the mapping between candidate update behaviors and solution quality through supervised or weakly supervised learning, enabling dynamic algorithm selection and the optimization of combinatorial strategies. This research attempts to address the limits of existing algorithm designs by investigating an automated algorithm generation strategy that incorporates Learning-to-Rank techniques. The goal is to achieve dynamic decision-making and adaptive adjustment within the algorithm generation process, thereby enhancing the generalization capability and solution efficiency of algorithms for various complex problems. By dynamically evaluating and ranking candidate solutions, LTR not only optimizes the algorithm's search strategies but also effectively guides the algorithm in avoiding local optima and accelerating global convergence. Consequently, it offers a solution that is both theoretically innovative and practically valuable. This work focuses on validating the feasibility of the multi-algorithm generation strategy in enhancing single metaheuristic methods, rather than aiming to identify an optimal combination. To this goal, three exemplary classical metaheuristic algorithms (the GA, WOA, and HHO) are selected for the study. The key contributions of this work are the following:  
* An automated hybrid metaheuristic algorithm design method based on Learning to Rank, called the LTR-MHA, is proposed. The method uses LTR to integrate different metaheuristic algorithms, such as the WOA, HHO, and the GA, allowing for dynamic algorithm selection and collaborative optimization. The LTR-MHA can flexibly integrate the update strategies of different algorithms based on problem features and dynamic feedback during the search process, considerably improving search capability and solution quality. This approach offers a novel and effective pathway for automated algorithm design and optimization. * The LTR-MHA constructs a training dataset by extracting key feature information from historical search processes and trains a predictive model to capture the mapping between candidate update behaviors and solution quality. During the search process, the feature vectors of search agents are fed into the trained model in real time to dynamically evaluate and rank candidate update behaviors. This enables the algorithm to prioritize more promising update actions, thereby enhancing the specificity and scientific basis of its search decisions. * The LTR-MHA method is empirically evaluated on four representative function categories from the CEC2017 benchmark.