Automated Generation of Hybrid Metaheuristics Using Learning-to-Rank

Metaheuristic algorithms, due to their superior global exploration capabilities and applicability, have emerged as critical tools for addressing complicated optimization tasks. However, these algorithms commonly depend on expert knowledge to configure parameters and design strategies. As a result, they frequently lack appropriate automatic behavior adjustment methods for dealing with changing problem features or dynamic search phases, limiting their adaptability, search efficiency, and solution quality. To address these limitations, this paper proposes an automated hybrid metaheuristic algorithm generation method based on Learning to Rank (LTR-MHA). The LTR-MHA aims to achieve adaptive optimization of algorithm combination strategies by dynamically fusing the search behaviors of Whale Optimization (WOA), Harris Hawks Optimization (HHO), and the Genetic Algorithm (GA). At the core of the LTR-MHA is the utilization of Learning-to-Rank techniques to model the mapping between problem features and algorithmic behaviors, to assess the potential of candidate solutions in real-time, and to guide the algorithm to make better decisions in the search process, thereby achieving a well-adjusted balance between the exploration and exploitation stages. The effectiveness and efficiency of the LTR-MHA method are evaluated using the CEC2017 benchmark functions. The experiments confirm the effectiveness of the proposed method. It delivers superior results compared to individual metaheuristic algorithms and random combinatorial strategies. Notable improvements are seen in average fitness, solution precision, and overall stability. Our approach offers a promising direction for efficient search capabilities and adaptive mechanisms in automated algorithm design.