Deep Learning and Hybrid-Metaheuristics: Novel Engineering Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".
Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 1363
Special Issue Editors
Interests: multi-objective optimization; structure optimization; reliability-based maintenance optimization; optimization and decision making under uncertainty
Special Issues, Collections and Topics in MDPI journals
Interests: optimization; deep learning; operations research; artificial intelligence applications to industrial problems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hybrid metaheuristic methods have shown very good performances in different combinatorial problems. Additionally, the rise of machine learning techniques has created a space to develop metaheuristic algorithms that use these techniques in order to tackle NP-hard problems and improve the convergence of algorithms. In this Special Issue, we invite researchers to submit papers in this optimization line, applying hybrid algorithms to industrial problems, including but not limited to industrial applications, and challenging problems arising in the fields of big data, construction, sustainability, transportation, and logistics, among others.
Deep learning techniques have also been important tools in extracting features, classifying situations, predicting events, and assisting in decision making. Some of these tools have been applied, for example, to Industry 4.0. Among the main techniques used are feedforward networks (FNN), convolutional networks (CNN), long-term short memory (LSTM), autoencoders (AE), generative adversarial networks, and deep Q-networks (DQNs). Contributions on practical deep learning applications and cases are invited to this Special Issue, including but not limited to applications to the industry of computational vision, natural language processing, supervised learning applied to industry, unsupervised learning applied to industry, and reinforcement learning, among others.
Prof. Dr. Víctor Yepes
Dr. José Antonio García
Guest Editors
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Keywords
- Construction
- Smart cities
- Optimization
- Deep learning
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