Complexity

Solving Engineering and Science Problems Using Complex Bio-inspired Computation Approaches


Publishing date
01 Jan 2021
Status
Closed
Submission deadline
21 Aug 2020

Lead Editor

1Shenzhen Institutes of Advanced Technology - Chinese Academy of Sciences, Shenzhen, China

2University of Warwick, Coventry, UK

3Gachon University, Seongnam-si, Republic of Korea

4Annamalai University, Chidambaram, India

This issue is now closed for submissions.
More articles will be published in the near future.

Solving Engineering and Science Problems Using Complex Bio-inspired Computation Approaches

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Bio-inspired computing (BIC) focuses on the design and development of computer algorithms and models based on complex biological mechanisms and living phenomena. BIC methods such as neural networks, genetic algorithms, and artificial immune systems are canonical complexity approaches and have proven powerful in solving science and engineering problems. However, emerging problems in complex systems such as energy, transportation, and logistic systems are large-scale, strongly non-linear, and highly coupling, and significantly challenge the current computational tools.

This Special Issue aims at soliciting both high-quality original research and review articles from the recently emerging and cross-disciplinary field of utilizing bio-inspired computation approaches in the complexity area to solve science and engineering problems. It will bring together researchers in relevant areas to present the latest progress, new computation approaches, and potential research applications. All original papers related to bio-inspired computation approaches and their application for modeling, control, and optimization of complex science and engineering problems are welcome.

Potential topics include but are not limited to the following:

  • Genetic algorithms, harmony search and other evolutionary algorithms
  • Neural networks, and other machine learning algorithms
  • Adaptive control
  • State estimation, fault diagnosis
  • Bio-inspired robot design
  • Bio-inspired computing in complexity for solving power and energy problems
  • Bio-inspired computing in complexity for solving transportation problems
  • Bio-inspired computing in complexity for solving manufacturing problems
  • Bio-inspired computing in complexity for solving financial problems
  • Bio-inspired computing in complexity for solving smart vehicles
Complexity
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Acceptance rate43%
Submission to final decision64 days
Acceptance to publication35 days
CiteScore3.300
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.