Special Issue "Swarm Information Acquisition and Swarm Intelligence in Engineering"
A special issue of Information (ISSN 2078-2489).
Deadline for manuscript submissions: closed (1 June 2015)
Dr. Baozhen Yao
School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China
Interests: artificial intelligence; urban logistics; public transportation
Prof. Dr. Yudong Zhang
Department of Informatics, University of Leicester, University Road, Leicester, LE1 7RH, UK
Website | E-Mail
Interests: deep learning; convolutional neural network; biomedical image analysis; bio-inspired computing; pattern recognition; transfer learning; image processing; artificial intelligence; machine learning; computer vision; swarm intelligence; particle swarm optimization; genetic algorithm; artificial bee colony; biogeography-based optimization; k-nearest neighbors
Swarm intelligence (SI) is an artificial intelligence technique based on the study of the behavior of simple individuals (e.g., ant colonies, bird flocking, animal herding and honey bees) in various decentralized systems. The population, which consists of simple individuals, can usually solve complex tasks in nature by individuals interacting locally with one another and with their environment. Although their behaviors are primarily characterized by autonomy, distributed functioning and self-organizing capacities, local interactions among the individuals often cause a global optimal.
Recently, SI algorithms have attracted much attention from researchers and have also been applied successfully to solve optimization problems in engineering. However, for large and complex problems, SI algorithms consume often much computation time due to stochastic feature of the search approaches. Therefore, there is a potential requirement to develop an efficient algorithm to find solutions under limited time and financial resources in real-world applications.
The aim of this special issue is to highlight the most significant recent developments in the topics of SI and to apply SI algorithms in a real-life scenario. Contributions containing new insights and findings in this field are welcome.
Dr. Baozhen Yao
Prof. Dr. Yudong Zhang
Manuscript Submission Information
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- benchmarking and evaluation of new si algorithms
- convergence proof for si algorithms
- comparative theoretical and empirical studies on si algorithms (e.g., ant colony optimization, particle swarm optimization, artificial bee swarm algorithm, bacterial foraging optimization, artificial fish algorithm, …)
- si algorithms for real-world applications