Nonlinear Optimization: Algorithmic Advances and Innovative Applications

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 168

Special Issue Editor


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Guest Editor
Department of Management Science and Information Systems, Rutgers Business School, Rutgers University, 100 Rockafeller Rd, Piscataway, NJ 08854, USA
Interests: business analytics; nonlinear optimization; global optimization; stochastic programming; software development; applications

Special Issue Information

Dear Colleagues,

Nonlinear optimization—including local and global and deterministic and stochastic optimization paradigms—is applicable to a broad range of business, engineering, and scientific decision-making situations. We invite to this Special Issue of Algorithms state-of-the-art contributions that present algorithmic advancements and innovative applications that can solve practically important decision problems.

Nonlinear optimization problems frequently require the development of new models and algorithmic advances, implying theoretical and computational challenges. The objectives of this Special Issue are to highlight their interaction and to facilitate collaboration between researchers proposing new algorithmic approaches and their real-world applications. By combining theoretical rigor with heuristics and practical insights, we aim at contributing to the development of efficient solutions to hard nonlinear decision problems.

We invite contributions to this Special Issue that are related to the following topics:

  • Deterministic model development and optimization applied to real-world problems.
  • Stochastic simulation, stochastic programming, and simulation optimization applied to real-world problems.
  • Algorithmic advances: novel heuristics and optimization algorithms and methodologies.
  • Challenges and innovative solutions in the applications of nonlinear optimization.
  • Interdisciplinary approaches: contributions that bridge disciplines—such as business analytics, management science, operations research, industrial engineering, and computer science—to address complex nonlinear optimization applications.
  • Integration of emerging technologies (such as artifical intelligence, machine learning, etc.) into optimization algorithms to enhance their applicability and efficiency.

Dr. János D. Pintér
Guest Editor

Manuscript Submission Information

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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. Algorithms 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 1800 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

  • nonlinear optimization
  • deterministic and stochastic models
  • global and local optimization
  • optimization algorithms
  • exact and heuristic approaches
  • simulation optimization
  • business, engineering, and scientific applications

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Published Papers (1 paper)

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Research

26 pages, 10740 KiB  
Article
A Nonlinear Computational Framework for Optimizing Steel End-Plate Connections Using the Finite Element Method and Genetic Algorithms
by Péter Grubits, Tamás Balogh and Majid Movahedi Rad
Algorithms 2025, 18(8), 460; https://doi.org/10.3390/a18080460 - 24 Jul 2025
Abstract
The design of steel connections presents considerable complexity due to their inherently nonlinear behavior, cost constraints, and the necessity to comply with structural design codes. These factors highlight the need for advanced computational algorithms to identify optimal solutions. In this study, a comprehensive [...] Read more.
The design of steel connections presents considerable complexity due to their inherently nonlinear behavior, cost constraints, and the necessity to comply with structural design codes. These factors highlight the need for advanced computational algorithms to identify optimal solutions. In this study, a comprehensive computational framework is presented in which the finite element method (FEM) is integrated with a genetic algorithm (GA) to optimize material usage in bolted steel end-plate joints, while structural safety is ensured based on multiple performance criteria. By incorporating both material and geometric nonlinearities, the mechanical response of the connections is accurately captured. The proposed approach is applied to a representative beam-to-column assembly, with numerical results verified against experimental data. By employing the framework, an optimized layout is obtained, yielding a 10.4% improvement in the overall performance objective compared to the best-performing validated model and a 39.3% reduction in material volume relative to the most efficient feasible alternative. Furthermore, a 53.6% decrease in equivalent plastic strain is achieved compared to the configuration exhibiting the highest level of inelastic deformation. These findings demonstrate that the developed method is capable of enhancing design efficiency and precision, underscoring the potential of advanced computational tools in structural engineering applications. Full article
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