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

Sayat Ibrayev1, Batyrkhan Omarov1,2,3,*, Arman Ibrayeva1, Zeinel Momynkulov1,2

1 Joldasbekov Institute of Mechanics and Engineering, Almaty, 050000, Kazakhstan
2 Department of Mathematical and Computer Modeling, International Information Technology University, Almaty, 050000, Kazakhstan
3 Department of Information Systems, Al-Farabi Kazakh National University, Almaty, 050000, Kazakhstan

* Corresponding Author: Batyrkhan Omarov. Email: email

Computers, Materials & Continua 2024, 81(1), 229-249. https://doi.org/10.32604/cmc.2024.053075

Abstract

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.

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APA Style
Ibrayev, S., Omarov, B., Ibrayeva, A., Momynkulov, Z. (2024). Deepsurnet-nsga II: deep surrogate model-assisted multi-objective evolutionary algorithm for enhancing leg linkage in walking robots. Computers, Materials & Continua, 81(1), 229-249. https://doi.org/10.32604/cmc.2024.053075
Vancouver Style
Ibrayev S, Omarov B, Ibrayeva A, Momynkulov Z. Deepsurnet-nsga II: deep surrogate model-assisted multi-objective evolutionary algorithm for enhancing leg linkage in walking robots. Comput Mater Contin. 2024;81(1):229-249 https://doi.org/10.32604/cmc.2024.053075
IEEE Style
S. Ibrayev, B. Omarov, A. Ibrayeva, and Z. Momynkulov, “DeepSurNet-NSGA II: Deep Surrogate Model-Assisted Multi-Objective Evolutionary Algorithm for Enhancing Leg Linkage in Walking Robots,” Comput. Mater. Contin., vol. 81, no. 1, pp. 229-249, 2024. https://doi.org/10.32604/cmc.2024.053075



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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