Novel Research and Applications on Optimization Algorithms

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 653

Special Issue Editors


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Guest Editor
Department of Engineering, Merchant Marine Academy of Aspropyrgos, 193 00 Aspropyrgos, Greece
Interests: swarm optimization; artificial intelligence; bioinspired optimization algorithms; power systems; renewable energy sources; electric load forecasting; wind speed prediction; high voltage

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Guest Editor
Department of Electrical and Electronics Engineering Educators, ASPETE—School of Pedagogical and Technological Education of Athens, 14121 Heraklion, Greece
Interests: power system protection; electrical power engineering; power systems simulation; power systems analysis; simulation; electrical engineering; engineering, applied and computational mathematics; transformers; power engineering; power transmission; electrostatic discharge; electromagnetic compatibility; high voltages
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Guest Editor
Department of Electrical and Electronic Engineering Educators, School of Pedagogical and Technological Education (ASPETE), N. Heraklion Attikis, 141 21 Athens, Greece
Interests: transmission and distribution grids; electric vehicles; distributed generation; energy storage, energy markets; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to present to the scientific community novel research and developments in the field of optimization, and we believe that your expertise would greatly contribute to our understanding of this important area.

Optimization plays a key role in tackling complex real-world problems across various areas, such as logistics, transportation, finance, healthcare, engineering, navigation and communication, environmental sciences, and many more. When researchers develop or improve the existing optimization techniques, they contribute to both the efficiency and effectiveness of solutions to the above-mentioned problems in terms of resource consumption, cost saving, and decision-making. Furthermore, by making use of recent technological advances, new approaches in optimization may lead to reduced training times, more accurate predictions, and more efficient use of computational resources, which are essential for the development of cutting-edge applications in many fields. Additionally, as challenges in natural sciences over the last years have become more complicated, optimization algorithms contribute to a more sustainable future by planning renewable energy production and thus reducing greenhouse gas emissions.

We invite cutting-edge research and both theoretical and experimental studies exploring recent advances in this field.

Prof. Dr. Stylianos Pappas
Dr. Georgios Fotis
Dr. Vasiliki Vita
Guest Editors

Manuscript Submission Information

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Keywords

  • advances in optimization
  • artificial intelligence
  • machine learning
  • deep learning
  • neural networks
  • swarm optimization
  • genetic algorithms
  • processing engineering application data

Published Papers (1 paper)

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Research

24 pages, 4670 KiB  
Article
AI-Based Decision Support System Optimizing Wireless Sensor Networks for Consumer Electronics in E-Commerce
by Mohammed Salem Basingab, Hatim Bukhari, Suhail H. Serbaya, Georgios Fotis, Vasiliki Vita, Stylianos Pappas and Ali Rizwan
Appl. Sci. 2024, 14(12), 4960; https://doi.org/10.3390/app14124960 - 7 Jun 2024
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Abstract
The purpose of this study is to investigate the potential of AI technology in developing a decision support system that can improve the effectiveness of wireless sensor networks (WSNs) in e-commerce, specifically in enhancing the features of consumer electronics. This research project is [...] Read more.
The purpose of this study is to investigate the potential of AI technology in developing a decision support system that can improve the effectiveness of wireless sensor networks (WSNs) in e-commerce, specifically in enhancing the features of consumer electronics. This research project is focused on optimizing wireless sensor networks for e-commerce consumer electronics by incorporating AI-based decision support systems. The primary objective of this study is to enhance energy efficiency and performance in online shopping platforms. Various algorithms and methodologies are proposed and assessed, including Adaptive Clustering, the Path Selection Algorithm, Fuzzy Logic-Controlled Energy Management, the Genetic Algorithm for Resource Allocation, and Deep Sleep Scheduling. These techniques improve network efficiency and reduce power consumption in e-commerce applications. The study demonstrates that integrating AI in consumer electronics can result in a remarkable 40% increase in energy efficiency. Comparative analyses conducted through simulations and real-world assessments indicate that the proposed methodology outperforms traditional techniques by 35%. This research underscores the vital role of AI in enhancing network performance and energy efficiency in e-commerce. The results suggest that implementing AI-driven strategies in wireless sensor networks for consumer electronics can significantly improve online shopping experiences. AI-based decision support systems can optimize wireless sensor networks for consumer electronics, improving energy efficiency and network performance on online shopping platforms. Full article
(This article belongs to the Special Issue Novel Research and Applications on Optimization Algorithms)
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