Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
4.5
Journals
Biomimetics
Special Issues
Computer-Aided Biomimetics: 2nd Edition
share announcement

Computer-Aided Biomimetics: 2nd Edition

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Biological Optimisation and Management".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 776

Special Issue Editors

Prof. Dr. Yong Peng

E-Mail Website
Guest Editor
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Interests: bionic structure design; traffic and vehicle crash safety; simulation; optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Danqi Wang

E-Mail Website
Guest Editor
School of Automobile and Mechanical Engineering, Changsha University of Science and Technology, Changsha, China
Interests: conceptual design; structural safety and energy saving design; multidisciplinary optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Dongkai Chu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shandong University, Jinan, China
Interests: bionic structure design, optimization, and processing
Special Issues, Collections and Topics in MDPI journals
Dr. Honghao Zhang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shandong University, Jinan, China
Interests: bionic structure design; biomaterials; numerical simulation; multidisciplinary optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer-aided biomimetics is an interdisciplinary research field that combines computer science and biomimetics. Drawing inspiration from nature's excellent designs, computer-aided biomimetics utilizes computer modeling and simulation techniques to mimic biological systems and apply the derived design and optimization insights to engineering and scientific fields. It finds wide-ranging applications and potential in areas such as materials science, mechanical engineering, aerospace, medicine, energy, and many more. The emergence of computer-aided biomimetics opens up new possibilities for engineers and scientists to tackle real-world problems, and holds the potential to drive technological innovation and scientific progress in the future. Many advanced technologies have been applied to the field of computer-aided biomimetics.

The purpose of this Special Issue is to incorporate the latest research studies in the field of advanced methods and applications, from either theoretical or practical perspectives. The relevant topics for this Special Issue include but are not limited to the following areas:

  • Multi-scale modeling and design of material structures;
  • Conceptual design and bio-structure design;
  • Bionic functional surface and bionic structure processing;
  • Mechanical structure and motion control of robots based on the bionics principle;
  • Performance analysis and evaluation of computer-aided biomimetics;
  • Multidisciplinary optimization algorithms for computer-aided biomimetics;
  • Application of AI in computer-aided biomimetics;
  • Other related research topics.

Dr. Honghao Zhang
Prof. Dr. Yong Peng
Dr. Danqi Wang
Dr. Dongkai Chu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomimetics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computer modeling
  • numerical simulation
  • bionic structures
  • design and optimization
  • motion control
  • bionic functional surface
  • performance analysis

Related Special Issue

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 4600 KiB  
Article
An Enhanced Tree-Seed Algorithm for Function Optimization and Production Optimization
by Qingan Zhou, Rong Dai, Guoxiao Zhou, Shenghui Ma and Shunshe Luo
Biomimetics 2024, 9(6), 334; https://doi.org/10.3390/biomimetics9060334 - 31 May 2024
Abstract
As the fields of engineering, energy, and geology become increasingly complex, decision makers face escalating challenges that require skilled solutions to meet practical production needs. Evolutionary algorithms, inspired by biological evolution, have emerged as powerful methods for tackling intricate optimization problems without relying [...] Read more.
As the fields of engineering, energy, and geology become increasingly complex, decision makers face escalating challenges that require skilled solutions to meet practical production needs. Evolutionary algorithms, inspired by biological evolution, have emerged as powerful methods for tackling intricate optimization problems without relying on gradient data. Among these, the tree-seed algorithm (TSA) distinguishes itself due to its unique mechanism and efficient searching capabilities. However, an imbalance between its exploitation and exploration phases can lead it to be stuck in local optima, impeding the discovery of globally optimal solutions. This study introduces an improved TSA that incorporates water-cycling and quantum rotation-gate mechanisms. These enhancements assist the algorithm in escaping local peaks and achieving a more harmonious balance between its exploitation and exploration phases. Comparative experimental evaluations, using the CEC 2017 benchmarks and a well-known metaheuristic algorithm, demonstrate the upgraded algorithm’s faster convergence rate and enhanced ability to locate global optima. Additionally, its application in optimizing reservoir production models underscores its superior performance compared to competing methods, further validating its real-world optimization capabilities. Full article
(This article belongs to the Special Issue Computer-Aided Biomimetics: 2nd Edition)
►▼ Show Figures

Figure 1

32 pages, 905 KiB  
Article
Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning
by Rodrigo Olivares, Omar Salinas, Camilo Ravelo, Ricardo Soto and Broderick Crawford
Biomimetics 2024, 9(6), 307; https://doi.org/10.3390/biomimetics9060307 - 21 May 2024
Viewed by 445
Abstract
In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms—namely Particle Swarm Optimization, [...] Read more.
In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms—namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm—with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning’s potential to boost cybersecurity measures in rapidly evolving threat environments. Full article
(This article belongs to the Special Issue Computer-Aided Biomimetics: 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop