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