Nature-Inspired Computer Algorithms

Dear Colleagues,

In recent years, hard optimization problems that cannot be solved within an acceptable computational time by deterministic mathematical optimization methods have been successfully solved by nature-inspired computer algorithms.

These kinds of algorithms are usually inspired by real-world phenomena and solve optimization problems by simulating physical rules or biological phenomena. For example, one of the most classical nature-inspired algorithms—genetic algorithms—incorporates natural operators of selection, crossover, and mutation in its search process.

Due to their simple structure and low requirements for target problems, nature-inspired computer algorithms are widely used to solve engineering, prediction, and optimal control problems, as well as clustering, classification, and deep learning problems. They are specifically designed to search for, generate, or select heuristic results that provide a sufficient solution to an optimization problem, especially with incomplete information or limited computational power.

The aim of this Special Issue is to encourage researchers to discuss topics surrounding the theories and applications based on nature-inspired computer algorithms. Topics of interest for this symposium include, but are not limited to:

• Construction and theoretical analysis of some novel or unique nature-inspired computer algorithms;
• Applications of nature-inspired computer algorithms for real-world problems;
• Strategies or ideas to enhance the nature-inspired computer algorithms’ capacities by combining the structural characteristics of real-world problems and biological phenomena of algorithms.

Prof. Dr. Gang Hu
Prof. Dr. Weiguo Zhao
Dr. Zhenxing Zhang
Guest Editors

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