DeepSurNet-NSGA II: Deep Surrogate Model-Assisted Multi-Objective Evolutionary Algorithm for Enhancing Leg Linkage in Walking Robots

• Published in: Computers, materials & continua Vol. 81; no. 1; pp. 229 - 249
• Main Authors: Ibrayev, Sayat, Omarov, Batyrkhan, Ibrayeva, Arman, Momynkulov, Zeinel
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 2024
• Subjects: Design optimization; Evolutionary algorithms; Genetic algorithms; Machine learning; Multiple objective analysis; Pareto optimization; Pareto optimum; Performance evaluation; Sorting algorithms
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2024.053075

This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II (Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II) for solving complex multi-objective optimization problems, with a particular focus on robotic leg-linkage design. The study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II, aiming to enhance the efficiency and precision of the optimization process. Through a series of empirical experiments and algorithmic analyses, the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods, underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational demands. The methodology encompasses a detailed exploration of the algorithm’s configuration, the experimental setup, and the criteria for performance evaluation, ensuring the reproducibility of results and facilitating future advancements in the field. The findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design optimization. By bridging the gap between complex optimization challenges and achievable solutions, this research contributes valuable insights into the optimization domain, offering a promising direction for future inquiries and technological innovations.