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Optimization Algorithms for Engineering Applications
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Optimization Algorithms for Engineering Applications

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 4689

Special Issue Editors

Prof. Dr. Heming Jia

E-Mail Website
Guest Editor
School of Information Engineering, Sanming University, Sanming 365004, China
Interests: Remora Optimization Algorithm (ROA); bio-inspired computing; nature-inspired computing; swarm intelligence; artificial intelligence; meta-heuristic modeling and optimization algorithms; evolutionary computations; multilevel image segmentation; feature selection; combinatorial problems
Special Issues, Collections and Topics in MDPI journals
Dr. Laith Abualigah

E-Mail Website
Guest Editor
Prince Hussein Bin Abdullah College for Information Technology, Al Al-Bayt University, Mafraq 130040, Jordan
Interests: arithmetic optimization algorithm (AOA); bio-inspired computing; nature-inspired computing; swarm intelligence; artificial intelligence; meta-heuristic modeling; optimization algorithms; evolutionary computations; information retrieval; text clustering; feature selection; combinatorial problems; optimization; advanced machine learning; big data; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Engineering application refers to applying theoretical knowledge, science, technology, scientific research results, experimental results, inventions, etc., through industrialization in the actual engineering of production, manufacturing, and construction processes. Various engineering applications are complex real-world problems. They are generally motivated by the ideas of evolution, human and animal behavior, or MPC (mathematics, physics, and chemistry). Optimization algorithms are widely acknowledged in the application of solving complex problems, such as non-convex, nonlinear constraints, and high-dimensional problems. There is a lot of literature on optimization algorithms, and they all eventually find the optimal solution through an exploration and exploitation process. Due to the stochastic nature of optimization algorithms, accurate and adequate results can be produced at a small cost.

This Special Issue intends to capture recent contributions of high-quality papers focusing on interdisciplinary research on the optimization algorithm for engineering applications using modern computational intelligence theories, approaches, and experiments. We invite the researchers to submit their original contributions addressing particular challenging aspects of optimization algorithms from theoretical and applied viewpoints. The topics of this Special Issue include (but are not limited to) the following:

  • Optimization algorithms
  • Swarm intelligence
  • Meta-heuristics
  • Engineering applications
  • Engineering design problems
  • Feature selection
  • Image segmentation
  • Real-world applications
  • Constraint handling
  • Benchmarks
  • Novel Approaches
  • Complicated Optimization Problems
  • Industrial Problems

Prof. Dr. Heming Jia
Dr. Laith Abualigah
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. Information is an international peer-reviewed open access monthly 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 1600 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

  • optimization algorithms
  • engineering application
  • metaheuristic algorithms

Published Papers (3 papers)

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21 pages, 3674 KiB  
Article
Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization
by Yaxuan Xu, Jianuo Liu, Zhongqi Cui, Ziying Liu, Chenxu Dai, Xiangzhen Zang and Zhanlin Ji
Information 2024, 15(4), 225; https://doi.org/10.3390/info15040225 - 17 Apr 2024
Viewed by 499
Abstract
With the continuous increase in global energy demand and growing environmental awareness, the utilization of renewable energy has become a worldwide consensus. In order to address the challenges posed by the intermittent and unpredictable nature of renewable energy in distributed power distribution networks, [...] Read more.
With the continuous increase in global energy demand and growing environmental awareness, the utilization of renewable energy has become a worldwide consensus. In order to address the challenges posed by the intermittent and unpredictable nature of renewable energy in distributed power distribution networks, as well as to improve the economic and operational stability of distribution systems, this paper proposes the establishment of an active distribution network capable of accommodating renewable energy. The objective is to enhance the efficiency of new energy utilization. This study investigates optimal scheduling models for energy storage technologies and economic-operation dispatching techniques in distributed power distribution networks. Additionally, it develops a comprehensive demand response model, with real-time pricing and incentive policies aiming to minimize load peak–valley differentials. The control mechanism incorporates time-of-use pricing and integrates a chaos particle swarm algorithm for a holistic approach to solution finding. By coordinating and optimizing the control of distributed power sources, energy storage systems, and flexible loads, the active distribution network achieves minimal operational costs while meeting demand-side power requirements, striving to smooth out load curves as much as possible. Case studies demonstrate significant enhancements during off-peak periods, with an approximately 60% increase in the load power overall elevation of load factors during regular periods, as well as a reduction in grid loads during evening peak hours, with a maximum decrease of nearly 65 kW. This approach mitigates grid operational pressures and user expense, effectively enhancing the stability and economic efficiency in distribution network operations. Full article
(This article belongs to the Special Issue Optimization Algorithms for Engineering Applications)
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15 pages, 4690 KiB  
Article
Scheduling for the Flexible Job-Shop Problem with a Dynamic Number of Machines Using Deep Reinforcement Learning
by Yu-Hung Chang, Chien-Hung Liu and Shingchern D. You
Information 2024, 15(2), 82; https://doi.org/10.3390/info15020082 - 1 Feb 2024
Viewed by 1303
Abstract
The dynamic flexible job-shop problem (DFJSP) is a realistic and challenging problem that many production plants face. As the product line becomes more complex, the machines may suddenly break down or resume service, so we need a dynamic scheduling framework to cope with [...] Read more.
The dynamic flexible job-shop problem (DFJSP) is a realistic and challenging problem that many production plants face. As the product line becomes more complex, the machines may suddenly break down or resume service, so we need a dynamic scheduling framework to cope with the changing number of machines over time. This issue has been rarely addressed in the literature. In this paper, we propose an improved learning-to-dispatch (L2D) model to generate a reasonable and good schedule to minimize the makespan. We formulate a DFJSP as a disjunctive graph and use graph neural networks (GINs) to embed the disjunctive graph into states for the agent to learn. The use of GINs enables the model to handle the dynamic number of machines and to effectively generalize to large-scale instances. The learning agent is a multi-layer feedforward network trained with a reinforcement learning algorithm, called proximal policy optimization. We trained the model on small-sized problems and tested it on various-sized problems. The experimental results show that our model outperforms the existing best priority dispatching rule algorithms, such as shortest processing time, most work remaining, flow due date per most work remaining, and most operations remaining. The results verify that the model has a good generalization capability and, thus, demonstrate its effectiveness. Full article
(This article belongs to the Special Issue Optimization Algorithms for Engineering Applications)
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22 pages, 473 KiB  
Article
Optimal Load Redistribution in Distribution Systems Using a Mixed-Integer Convex Model Based on Electrical Momentum
by Daniela Patricia Bohórquez-Álvarez, Karen Dayanna Niño-Perdomo and Oscar Danilo Montoya
Information 2023, 14(4), 229; https://doi.org/10.3390/info14040229 - 7 Apr 2023
Cited by 1 | Viewed by 1420
Abstract
This paper addresses the problem concerning the efficient minimization of power losses in asymmetric distribution grids from the perspective of convex optimization. This research’s main objective is to propose an approximation optimization model to reduce the total power losses in a three-phase network [...] Read more.
This paper addresses the problem concerning the efficient minimization of power losses in asymmetric distribution grids from the perspective of convex optimization. This research’s main objective is to propose an approximation optimization model to reduce the total power losses in a three-phase network using the concept of electrical momentum. To obtain a mixed-integer convex formulation, the voltage variables at each node are relaxed by assuming them to be equal to those at the substation bus. With this assumption, the power balance constraints are reduced to flow restrictions, allowing us to formulate a set of linear rules. The objective function is formulated as a strictly convex objective function by applying the concept of average electrical momentum, by representing the current flows in distribution lines as the active and reactive power variables. To solve the relaxed MIQC model, the GAMS software (Version 28.1.2) and its CPLEX, SBB, and XPRESS solvers are used. In order to validate the effectiveness of load redistribution in power loss minimization, the initial and final grid configurations are tested with the triangular-based power flow method for asymmetric distribution networks. Numerical results show that the proposed mixed-integer model allows for reductions of 24.34%, 18.64%, and 4.14% for the 8-, 15-, and 25-node test feeders, respectively, in comparison with the benchmark case. The sine–cosine algorithm and the black hole optimization method are also used for comparison, demonstrating the efficiency of the MIQC approach in minimizing the expected grid power losses for three-phase unbalanced networks. Full article
(This article belongs to the Special Issue Optimization Algorithms for Engineering Applications)
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