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Applied Sciences
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Novel Research and Applications on Optimization Algorithms
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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 March 2025 | Viewed by 3571

Special Issue Editors

Prof. Dr. Stylianos Pappas

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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
Dr. Georgios Fotis

E-Mail Website
Guest Editor
Centre for Energy Technologies, Aarhus University, 7400 Herning, Denmark
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
Special Issues, Collections and Topics in MDPI journals
Dr. Vasiliki Vita

E-Mail Website
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Published Papers (3 papers)

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Research

26 pages, 2233 KiB  
Article
Exploring the Impact of Local Operator Configurations in the Multi-Demand Multidimensional Knapsack Problem
by José García, Ivo Cattarinich, Paola Moraga and Hernan Pinto
Appl. Sci. 2025, 15(4), 2059; https://doi.org/10.3390/app15042059 - 16 Feb 2025
Viewed by 223
Abstract
The Multi-demand Multidimensional Knapsack Problem (MDMKP) is a challenging combinatorial task due to its capacity and demand constraints. Local search operators play a key role in metaheuristics when navigating such complex solution spaces, yet their impact on MDMKP performance has received limited attention. [...] Read more.
The Multi-demand Multidimensional Knapsack Problem (MDMKP) is a challenging combinatorial task due to its capacity and demand constraints. Local search operators play a key role in metaheuristics when navigating such complex solution spaces, yet their impact on MDMKP performance has received limited attention. In this work, we investigate four local operator configurations—Add, Drop, Swap, and All Operator—within the Whale Optimization Algorithm framework. Our approach integrates these operators to broaden search coverage and refine candidate solutions. This design aims to enhance solution quality by balancing exploration and exploitation across multiple dimensions of the MDMKP. Experimental results on benchmark instances with different sizes (n=100,250, and 500) show that the All Operator configuration consistently achieves better maximum and average values. In large-scale instances (n = 500), the “All Operator” configuration achieves an average maximum value of 107,967, which is approximately 1.4% higher than the 106,490 achieved by the “Add Operator” and about 0.2% higher than the 107,771 obtained by the “Swap Operator”, while significantly outperforming the “Drop Operator” (average maximum of 99,164). Statistical tests confirm its advantage over the other configurations, suggesting that combining multiple local operators can significantly strengthen performance in high-dimensional and constraint-heavy settings like the MDMKP. Full article
(This article belongs to the Special Issue Novel Research and Applications on Optimization Algorithms)
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40 pages, 2390 KiB  
Article
An Elegant Multi-Agent Gradient Descent for Effective Optimization in Neural Network Training and Beyond
by Mohammad Sakka and Mohammad Reza Bahrami
Appl. Sci. 2025, 15(4), 2008; https://doi.org/10.3390/app15042008 - 14 Feb 2025
Viewed by 711
Abstract
Non-convex optimization problems often challenge gradient-based algorithms, such as Gradient Descent. Neural network training, a prominent application of gradient-based methods, heavily relies on their computational efficiency. However, the cost function in neural network training is typically non-convex, causing gradient-based algorithms to become trapped [...] Read more.
Non-convex optimization problems often challenge gradient-based algorithms, such as Gradient Descent. Neural network training, a prominent application of gradient-based methods, heavily relies on their computational efficiency. However, the cost function in neural network training is typically non-convex, causing gradient-based algorithms to become trapped in local minima due to their limited exploration of the solution space. In contrast, global optimization algorithms, such as swarm-based methods, provide better exploration but introduce significant computational overhead. To address these challenges, we propose Multi-Agent Gradient Descent (MAGD), a novel algorithm that combines the efficiency of gradient-based methods with enhanced exploration capabilities. MAGD initializes multiple agents, each representing a candidate solution, and independently updates their positions using gradient-based techniques without inter-agent communication. The number of agents is dynamically adjusted by removing underperforming agents to minimize computational cost. MAGD offers a cost-effective solution for non-convex optimization problems, including but not limited to neural network training. We benchmark MAGD against traditional Gradient Descent (GD), Adam, and Swarm-Based Gradient Descent (SBGD), demonstrating that MAGD achieves superior solution quality without a significant increase in computational complexity. MAGD outperforms these methods on 20 benchmark mathematical optimization functions and 20 real-world classification and regression datasets for training shallow neural networks. Full article
(This article belongs to the Special Issue Novel Research and Applications on Optimization Algorithms)
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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
Cited by 8 | Viewed by 1546
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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