Submission Deadline: 28 February 2025 View: 364 Submit to Special Issue
Dr. Ying Tian, Jilin Agricultural University, China
Prof. Dr. Gaige Wang, Ocean University of China, China
Swarm intelligence (SI) is a kind of optimization algorithms that are inspired by the behavior of organisms in nature. SI is currently experiencing rapid development, with applications spanning various domains including optimization problems, data analysis, scheduling, robotics, image processing, and biology. Within optimization problems, SI effectively tackles complex issues, such as the traveling salesman problem and resource allocation. Their advantages lie in handling high-dimensional, non-linear, and multimodal problems while possessing global optimization capabilities. In data analysis, SI finds utility in clustering analysis, pattern recognition, and data mining. SI is also utilized in biology to study and simulate group behaviors within biological systems. SI's wide-reaching applications span mathematics, machine learning, and biological studies. The domain continuously introduces improved formulations, algorithms, practical implementations, and theoretical insights, showcasing its dynamic research landscape. The main purpose is to delve into the latest research, applications, and future directions in the field of SI (such as particle swarm optimization, ant colony algorithms, artificial bee colony, etc.). It will focus on the innovations, applications, and potential of these algorithms in solving real-world problems.
This special issue intends to provide a timely chance for scientists, researchers, and engineers to discuss and summarize the latest methodologies, models, algorithms, and findings in SI. Submissions should be original and unpublished, and present novel in-depth fundamental research contributions. Both theoretical and experimental studies are welcome, as well as comprehensive reviews and surveys. Topic of interest for publication include but are not limited to the following topics:
(1) Improvement in traditional SI algorithms.
(2) Novel techniques development of traditional SI algorithms.
(3) Theoretical study on algorithms (e.g., genetic algorithms, evolutionary algorithms, multi-objective optimization, combinatorial optimization, bio-inspired optimization, differential evolution, and metaheuristics).
(4) Applications of mathematical optimization in big data analytics, scheduling, robotics, image processing, optimization of machine learning, and deep learning models.
(5) Innovative methodology related to SI.
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