Special Issue "Recent Advances in Swarm Intelligence Algorithms and Their Applications, 2nd Edition"

Dear Colleagues, 

As a branch of artificial intelligence, swarm intelligence refers to the collective behavior of decentralized, self-organized systems. Swarm intelligence mainly attracts and organizes the local interactions among participants and with their environment. It aims to jointly cope with challenging tasks by means of competition, cooperation, and other independent or collaborative ways, especially complex system decision-making tasks in the open environment, which lead to the emergence of intelligent global behavior, unknown to individuals.

In recent years, the research community has witnessed an increased number of swarm intelligence algorithms that can efficiently solve complex computation tasks. Algorithms for optimization problems have been focused on, mainly due to the unprecedented complexity of problems arising from a diverse spectrum of domains, such as transportation, logistics, energy, climate, social networks, health, and Industry 4.0, among many others.

This Special Issue provides a platform for researchers from academia and industry to present their new and unpublished work and to promote future studies in swarm intelligence and its combination with real-world problems and other fields, including, but not limited to, antenna design, vehicle scheduling, drug design and discovery, image segmentation, feature selection, data clustering, traveling salesman problems, etc.

Prof. Dr. Jian Dong
Guest Editor

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• swarm intelligence
• evolutionary algorithms
• optimization
• metaheuristics
• surrogate modeling
• differential evolution
• real-world applications
• machine learning
• optimal design
• benchmark functions