Advances in Biological and Bio-Inspired Algorithms

Dear Colleagues,

Optimization problems have always been a focus of research and have already existed in various real-world systems, such as fault diagnosis systems, energy management systems, and prediction systems. There are two main directions for the development of swarm intelligence optimization algorithms: one is to design new algorithms with a better performance; the other is to improve the update strategy of existing algorithms to achieve better optimization performance. Significant advancements have been made in biological and bio-inspired algorithms in the pursuit of increased flexibility and efficiency. Here, the ingenious systems seen in nature serve as the model for these algorithms. They are designed to mimic biological processes in order to enhance complex functions. Ongoing research and innovation have led to considerable improvements in their computational models and efficiency, with applications in a wide range of industries that are revolutionizing the way problems are resolved and systems are improved.

By improving the structural architecture of buildings, engineers may reduce material consumption while maintaining stability. They can dynamically alter data transmission channels in network routing to lower latency. In the field of energy management, they efficiently allocate electrical resources to balance supply and demand. These applications demonstrate how beneficial biological and bio-inspired algorithms are in a variety of fields.

Academic and industry research subjects include the development of algorithms in areas such as evolutionary computation inspired by natural selection, swarm intelligence based on social insect behavior, and neural network algorithms inspired by the structure of the human brain. The goal is to research and expand their potential applications. We look forward to your contributions.

Dr. Yinggao Yue
Guest Editor

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• biological algorithms
• bio-inspired algorithms
• optimization
• optimal design
• computational efficiency
• nature emulation
• problem-solving
• path optimization problem
• dynamic adaptation
• energy optimization
• power system optimization
• engineering optimization problem