| 1. |
- Burman, Erik, et al.
(author)
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The Augmented Lagrangian Method as a Framework for Stabilised Methods in Computational Mechanics
- 2023
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In: Archives of Computational Methods in Engineering. - : Springer. - 1134-3060 .- 1886-1784. ; 30, s. 2579-2604
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Journal article (peer-reviewed)abstract
- In this paper we will present a review of recent advances in the application of the augmented Lagrange multiplier method as a general approach for generating multiplier-free stabilised methods. The augmented Lagrangian method consists of a standard Lagrange multiplier method augmented by a penalty term, penalising the constraint equations, and is well known as the basis for iterative algorithms for constrained optimisation problems. Its use as a stabilisation methods in computational mechanics has, however, only recently been appreciated. We first show how the method generates Galerkin/Least Squares type schemes for equality constraints and then how it can be extended to develop new stabilised methods for inequality constraints. Application to several different problems in computational mechanics is given.
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| 2. |
- Das, Arunita, et al.
(author)
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Particle Swarm Optimizer Variants for Multi-level Thresholding: Theory, Performance Enhancement and Evaluation
- 2024
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In: Archives of Computational Methods in Engineering. - : SPRINGER. - 1134-3060 .- 1886-1784.
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Journal article (peer-reviewed)abstract
- Multilevel thresholding (MLT) has a significant impact in the realm of image segmentation. It is a simple and efficient method for image segmentation. However, as the number of thresholds increases, the computational complexity of MLT increases exponentially, and proper thresholding crucially depends on the utilized objective function. Swarm intelligence (SI) with the optimal objective function is used to enhance the efficacy of MLT image segmentation. Among the various SI techniques, Particle Swarm Optimization (PSO) and its variants are utilized extensively in literature to improve the performance of MLT. As a result, the objective of this study is to provide an updated survey on this topic. This study provides a comprehensive report of the classical PSO, and its improved variants, and their applications in MLT domains. However, like other SI techniques, PSO and its widely utilized variant called Darwinian PSO (DPSO) have the drawbacks of imbalanced exploration and exploitation and premature convergence. Therefore, along with the updated survey report, this study develops an efficient variant of the DPSO called Cauchy DPSO with Opposition Learning (CaDPSOOL). The Cauchy distribution and opposition learning (OL) have been incorporated to enhance the optimization capability by preventing premature convergence and maintaining the good balance between exploration and exploitation. Furthermore, a new hybrid objective function has also been designed by considering Otsu and Tsallis entropy in MLT domain for better segmentation results. The proposed CaDPSOOL with hybrid objective function has been employed for standard color image and hematopathology image segmentation domains. The results of this study show that the proposed model produces better results than other state-of-the-art MLT models in terms of segmentation quality metrics.
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| 3. |
- de Prenter, Frits, et al.
(author)
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Stability and conditioning of immersed finite element methods : analysis and remedies
- 2023
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In: Archives of Computational Methods in Engineering. - : Springer Nature. - 1134-3060 .- 1886-1784. ; 30, s. 3617-3656
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Research review (peer-reviewed)abstract
- This review paper discusses the developments in immersed or unfitted finite element methods over the past decade. The main focus is the analysis and the treatment of the adverse effects of small cut elements. We distinguish between adverse effects regarding the stability and adverse effects regarding the conditioning of the system, and we present an overview of the developed remedies. In particular, we provide a detailed explanation of Schwarz preconditioning, element aggregation, and the ghost penalty formulation. Furthermore, we outline the methodologies developed for quadrature and weak enforcement of Dirichlet conditions, and we discuss open questions and future research directions.
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| 4. |
- Huotari, Matti, et al.
(author)
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Machine learning applications for smart building energy utilization : a survey
- 2024
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In: Archives of Computational Methods in Engineering. - : Springer Nature. - 1134-3060 .- 1886-1784.
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Journal article (peer-reviewed)abstract
- The United Nations launched sustainable development goals in 2015 that include goals for sustainable energy. From global energy consumption, households consume 20–30% of energy in Europe, North America and Asia; furthermore, the overall global energy consumption has steadily increased in the recent decades. Consequently, to meet the increased energy demand and to promote efficient energy consumption, there is a persistent need to develop applications enhancing utilization of energy in buildings. However, despite the potential significance of AI in this area, few surveys have systematically categorized these applications. Therefore, this paper presents a systematic review of the literature, and then creates a novel taxonomy for applications of smart building energy utilization. The contributions of this paper are (a) a systematic review of applications and machine learning methods for smart building energy utilization, (b) a novel taxonomy for the applications, (c) detailed analysis of these solutions and techniques used for the applications (electric grid, smart building energy management and control, maintenance and security, and personalization), and, finally, (d) a discussion on open issues and developments in the field.
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| 5. |
- Parvaze, Sabah, et al.
(author)
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Optimization of Water Distribution Systems Using Genetic Algorithms: A Review
- 2023
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In: Archives of Computational Methods in Engineering. - : Springer. - 1134-3060 .- 1886-1784. ; 30, s. 4209-4244
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Research review (peer-reviewed)abstract
- Water distribution networks are crucial for supplying consumers with quality and adequate water. A water distribution system comprises connected hydraulic components which ensure water supply and distribution to meet demand. Optimization of water distribution networks is carried out to minimize resource utilization and expenditure or maximize the system’s efficiency and higher benefits. Genetic algorithms signify an effective search technique for non-linear optimization problems and have gained acceptance among water resources planners and managers. This paper reviews various developments in the optimization of water distribution systems using the technique of genetic algorithms. These developments are pertinent to creating novel systems for distributing water and the expansion, reinforcement, and rehabilitation process for prevailing water supply mechanisms.
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| 6. |
- Rodriguez Prieto, Juan Manuel, et al.
(author)
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Numerical Methods for the Modelling of Chip Formation
- 2020
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In: Archives of Computational Methods in Engineering. - : Springer. - 1134-3060 .- 1886-1784. ; 27:2, s. 387-412
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Journal article (peer-reviewed)abstract
- The modeling of metal cutting has proved to be particularly complex due to the diversity of physical phenomena involved, including thermo-mechanical coupling, contact/friction and material failure. During the last few decades, there has been significant progress in the development of numerical methods for modeling machining operations. Furthermore, the most relevant techniques have been implemented in the relevant commercial codes creating tools for the engineers working in the design of processes and cutting devices. This paper presents a review on the numerical modeling methods and techniques used for the simulation of machining processes. The main purpose is to identify the strengths and weaknesses of each method and strategy developed up-to-now. Moreover the review covers the classical Finite Element Method covering mesh-less methods, particle-based methods and different possibilities of Eulerian and Lagrangian approaches.
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| 7. |
- Sasmal, Buddhadev, et al.
(author)
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A Comprehensive Survey on Aquila Optimizer
- 2023
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In: Archives of Computational Methods in Engineering. - : SPRINGER. - 1134-3060 .- 1886-1784. ; 30, s. 4449-4476
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Journal article (peer-reviewed)abstract
- Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effectiveness in the field of complex and nonlinear optimization in a short period of time. As a result, the purpose of this study is to provide an updated survey on the topic. This survey accurately reports on the designed enhanced AO variations and their applications. In order to properly assess AO, a rigorous comparison between AO and its peer NIOAs is conducted over mathematical benchmark functions. The experimental results show the AO provides competitive outcomes.
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| 8. |
- Sasmal, Buddhadev, et al.
(author)
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Reptile Search Algorithm: Theory, Variants, Applications, and Performance Evaluation
- 2023
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In: Archives of Computational Methods in Engineering. - : SPRINGER. - 1134-3060 .- 1886-1784.
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Journal article (peer-reviewed)abstract
- Reptile Search Algorithm (RSA) is a recently developed nature-inspired meta-heuristics optimization algorithm inspired by the encircling mechanism, hunting mechanism and social behaviours of crocodiles in nature. Since Abualigah et al. introduced RSA in 2022, it has garnered significant interest from researchers and been widely employed to address various optimization challenges across a variety of fields. This is because it has an adequate execution time, an efficient convergence rate, and is more effective than other well-known optimization algorithms. As a result, the objective of this study is to provide an updated survey on the topic. This study provides a comprehensive report of the classical RSA, and its improved variants and their applications in various domains. To adequately analyse RSA, a comprehensive comparison among RSA and its peer NIOAs is performed using mathematical benchmark functions.
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