Advances on intelligent algorithms for scientific computing: an overview

Cheng Hua; Xinwei Cao; Bolin Liao; Shuai Li

doi:10.3389/fnbot.2023.1190977

Advances on intelligent algorithms for scientific computing: an overview

Front Neurorobot. 2023 Apr 21:17:1190977. doi: 10.3389/fnbot.2023.1190977. eCollection 2023.

Authors

Cheng Hua^#¹, Xinwei Cao^#², Bolin Liao¹, Shuai Li^{3

4}

Affiliations

¹ College of Computer Science and Engineering, Jishou University, Jishou, China.
² School of Business, Jiangnan University, Wuxi, China.
³ Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland.
⁴ VTT Technical Research Centre of Finland, Oulu, Finland.

^# Contributed equally.

Abstract

The field of computer science has undergone rapid expansion due to the increasing interest in improving system performance. This has resulted in the emergence of advanced techniques, such as neural networks, intelligent systems, optimization algorithms, and optimization strategies. These innovations have created novel opportunities and challenges in various domains. This paper presents a thorough examination of three intelligent methods: neural networks, intelligent systems, and optimization algorithms and strategies. It discusses the fundamental principles and techniques employed in these fields, as well as the recent advancements and future prospects. Additionally, this paper analyzes the advantages and limitations of these intelligent approaches. Ultimately, it serves as a comprehensive summary and overview of these critical and rapidly evolving fields, offering an informative guide for novices and researchers interested in these areas.

Keywords: dynamic systems; intelligent systems; neural networks; optimization algorithms and strategies; robotic.

Publication types

Review

Grants and funding

This work was supported in part by the National Natural Science Foundation of China under Grant No. 62066015, the Natural Science Foundation of Hunan Province of China under Grant No. 2020JJ4511, and the Research Foundation of Education Bureau of Hunan Province, China, under Grant No. 20A396.