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Mathematics
Special Issues
Multi-objective Optimization and Applications
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Multi-objective Optimization and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 3485

Special Issue Editors

Prof. Dr. Víctor Yepes

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Guest Editor
Construction Engineering Department, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: multi-objective optimization; structure optimization; reliability-based maintenance optimization; optimization and decision making under uncertainty
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Moacir Kripka

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Guest Editor
Civil Engineering Graduate Program, Federal University of Technology–Paraná, Via do Conhecimento, Km 1, Pato Branco 85503-390, Paraná, Brazil
Interests: structural analysis; optimization; engineering optimization; linear programming; mathematical programming; heuristics; structural optimization; concrete; combinatorial optimization; structural engineering; multiobjective optimization; reinforced concrete; optimization methods; discrete optimization; optimization theory; optimization software
Special Issues, Collections and Topics in MDPI journals
Dr. José Antonio García

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Guest Editor
Escuela de Ingeniería en Construcción, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile
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,

The use of optimization techniques has become frequent in recent decades, as a result of growing competitiveness brought about by globalization. With the development of new methods and the greater availability of computer resources, applications in the most diverse fields of knowledge have spread, from academic banks to the day-to-day running of companies. However, a more realistic approach can be achieved if several objectives are integrated into the process. Thus, an exciting strategy cannot only meet cost requirements, for example, but it also concerns itself with durability, efficiency, reliability, and sustainability. Given that several objectives are involved, which are usually in conflict with each other, new strategies are needed, while new and more complete applications are envisioned. In this sense, the Special Issue, “Multi-objective Optimization and Applications”, aims to provide a platform for the dissemination of knowledge related to multi-objective optimization. Research articles involving efficient and innovative optimization methods and new applications related to the diverse areas of expertise are sought, promoting an exchange of new ideas and trends in relation to the subject.

Prof. Dr. Víctor Yepes
Prof. Dr. Moacir Kripka
Dr. José Antonio García
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. Mathematics 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 2600 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

  • multi-objective optimization
  • optimization methods
  • optimization applications
  • innovative methods and algorithms

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Published Papers (2 papers)

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21 pages, 9299 KiB  
Article
Implementing PSO-LSTM-GRU Hybrid Neural Networks for Enhanced Control and Energy Efficiency of Excavator Cylinder Displacement
by Van-Hien Nguyen, Tri Cuong Do and Kyoung-Kwan Ahn
Mathematics 2024, 12(20), 3185; https://doi.org/10.3390/math12203185 - 11 Oct 2024
Viewed by 971
Abstract
In recent years, increasing attention has been given to reducing energy consumption in hydraulic excavators, resulting in extensive research in this field. One promising solution has been the integration of hydrostatic transmission (HST) and hydraulic pump/motor (HPM) configurations in parallel systems. However, these [...] Read more.
In recent years, increasing attention has been given to reducing energy consumption in hydraulic excavators, resulting in extensive research in this field. One promising solution has been the integration of hydrostatic transmission (HST) and hydraulic pump/motor (HPM) configurations in parallel systems. However, these systems face challenges such as noise, throttling losses, and leakage, which can negatively impact both tracking accuracy and energy efficiency. To address these issues, this paper introduces an intelligent real-time prediction framework for system positioning, incorporating particle swarm optimization (PSO), long short-term memory (LSTM), a gated recurrent unit (GRU), and proportional–integral–derivative (PID) control. The process begins by analyzing real-time system data using Pearson correlation to identify hyperparameters with medium to strong correlations to the positioning parameters. These selected hyperparameters are then used as inputs for forecasting models. Independent LSTM and GRU models are subsequently developed to predict the system’s position, with PSO optimizing four key hyperparameters of these models. In the final stage, the PSO-optimized LSTM-GRU models are employed to perform real-time intelligent predictions of motion trajectories within the system. Simulation and experimental results show that the model achieves a prediction deviation of less than 3 mm, ensuring precise real-time predictions and providing reliable data for system operators. Compared to traditional PID and LSTM-GRU-PID controllers, the proposed controller demonstrated superior tracking accuracy while also reducing energy consumption, achieving energy savings of up to 10.89% and 2.82% in experimental tests, respectively. Full article
(This article belongs to the Special Issue Multi-objective Optimization and Applications)
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30 pages, 2870 KiB  
Article
Enhanced Structural Design of Prestressed Arched Trusses through Multi-Objective Optimization and Multi-Criteria Decision-Making
by Andrés Ruiz-Vélez, José García, Gaioz Partskhaladze, Julián Alcalá and Víctor Yepes
Mathematics 2024, 12(16), 2567; https://doi.org/10.3390/math12162567 - 20 Aug 2024
Viewed by 1498
Abstract
The structural design of prestressed arched trusses presents a complex challenge due to the need to balance multiple conflicting objectives such as structural performance, weight, and constructability. This complexity is further compounded by the interdependent nature of the structural elements, which necessitates a [...] Read more.
The structural design of prestressed arched trusses presents a complex challenge due to the need to balance multiple conflicting objectives such as structural performance, weight, and constructability. This complexity is further compounded by the interdependent nature of the structural elements, which necessitates a comprehensive optimization approach. Addressing this challenge is crucial for advancing construction practices and improving the efficiency and safety of structural designs. The integration of advanced optimization algorithms and decision-making techniques offers a promising avenue for enhancing the design process of prestressed arched trusses. This study proposes the use of three advanced multi-objective optimization algorithms: NSGA-III, CTAEA, and SMS-EMOA, to optimize the structural design of prestressed arched trusses. The performance of these algorithms was evaluated using generational distance and inverted generational distance metrics. Additionally, the non-dominated optimal designs generated by these algorithms were assessed and ranked using multiple multi-criteria decision-making techniques, including SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR. This approach allowed for a robust comparison of the algorithms and provided insights into their effectiveness in balancing the different design objectives. The results of the study indicated that NSGA-III exhibited superior performance with a GD value of 0.215, reflecting a closer proximity of its solutions to the Pareto front, and an IGD value of 0.329, indicating a well-distributed set of solutions across the Pareto front. In comparison, CTAEA and SMS-EMOA showed higher GD values of 0.326 and 0.436, respectively, suggesting less convergence to the Pareto front. However, SMS-EMOA demonstrated a balanced performance in terms of constructability and structural weight, with an IGD value of 0.434. The statistical significance of these differences was confirmed by the Kruskal–Wallis test, with p-values of 2.50×10−15 for GD and 5.15×10−06 for IGD. These findings underscore the advantages and limitations of each algorithm, providing valuable insights for future applications in structural optimization. Full article
(This article belongs to the Special Issue Multi-objective Optimization and Applications)
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