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Georgios Kazakis and Nikos D. Lagaros
In multi-scale topology optimization methods, the analysis encompasses two distinct scales: the macro-scale and the micro-scale. The macro-scale refers to the overall size and dimensions of the structural domain being studied, while the micro-scale perta...
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Zhenglong Li and Vincent Tam
Meta-heuristic algorithms have successfully solved many real-world problems in recent years. Inspired by different natural phenomena, the algorithms with special search mechanisms can be good at tackling certain problems. However, they may fail to solve ...
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Nassime Aslimani, Talbi El-ghazali and Rachid Ellaia
Multi-objective optimization problems (MOPs) have been widely studied during the last decades. In this paper, we present a new approach based on Chaotic search to solve MOPs. Various Tchebychev scalarization strategies have been investigated. Moreover, a...
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Alexey Vakhnin and Evgenii Sopov
Many modern real-valued optimization tasks use ?black-box? (BB) models for evaluating objective functions and they are high-dimensional and constrained. Using common classifications, we can identify them as constrained large-scale global optimization (cL...
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Wendy Wijaya and Kuntjoro Adji Sidarto
Portfolio optimization is a mathematical formulation whose objective is to maximize returns while minimizing risks. A great deal of improvement in portfolio optimization models has been made, including the addition of practical constraints. As the number...
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Napsu Karmitsa, Sona Taheri, Kaisa Joki, Pauliina Paasivirta, Adil M. Bagirov and Marko M. Mäkelä
In this paper, a new nonsmooth optimization-based algorithm for solving large-scale regression problems is introduced. The regression problem is modeled as fully-connected feedforward neural networks with one hidden layer, piecewise linear activation, an...
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Aleksei Vakhnin and Evgenii Sopov
Unconstrained continuous large-scale global optimization (LSGO) is still a challenging task for a wide range of modern metaheuristic approaches. A cooperative coevolution approach is a good tool for increasing the performance of an evolutionary algorithm...
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Zabidin Salleh, Ghaliah Alhamzi, Ibitsam Masmali and Ahmad Alhawarat
The conjugate gradient method is one of the most popular methods to solve large-scale unconstrained optimization problems since it does not require the second derivative, such as Newton?s method or approximations. Moreover, the conjugate gradient method ...
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Aleksei Vakhnin and Evgenii Sopov
Modern real-valued optimization problems are complex and high-dimensional, and they are known as ?large-scale global optimization (LSGO)? problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dim...
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Manuel Lara, Juan Garrido, Mario L. Ruz and Francisco Vázquez
This paper deals with the control problems of a wind turbine working in its nominal zone. In this region, the wind turbine speed is controlled by means of the pitch angle, which keeps the nominal power constant against wind fluctuations. The non-uniform ...
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Sadik Ozgur Degertekin, Mohammad Minooei, Lorenzo Santoro, Bartolomeo Trentadue and Luciano Lamberti
Metaheuristic algorithms currently represent the standard approach to engineering optimization. A very challenging field is large-scale structural optimization, entailing hundreds of design variables and thousands of nonlinear constraints on element stre...
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Zhizhuo Cui and Fuzhou Du
For assembly incoordination caused by excessive assembly deviations, the proposed method can predict the assemblability and solve the assembly features that need accuracy compensation, to improve the assembly efficiency.
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John P. Jasa, Benjamin J. Brelje, Justin S. Gray, Charles A. Mader and Joaquim R. R. A. Martins
Aircraft are multidisciplinary systems that are challenging to design due to interactions between the subsystems. The relevant disciplines, such as aerodynamic, thermal, and propulsion systems, must be considered simultaneously using a path-dependent for...
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Sang Hyun Lee, Dong-Ha Lim and Kyungtae Park
In this study, exergy and economic analysis were conducted to gain insight on small-scale movable LNG liquefaction considering leakage. Optimization and comparison were performed to demonstrate the quantitative results of single mixed refrigerant, dual n...
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Christos Karydas and Bin Jiang
A new method for selecting optimal scales when mapping topographic or hydrographic features is introduced. The method employs rank-size partition of heavy-tailed distributions to detect nodes of rescaling invariance in the underlying hierarchy of the dat...
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Yiyuan Sun, Qiang Wang, Kevin Tansey, Sana Ullah, Fan Liu, Haimeng Zhao and Lei Yan
Image-based line segment extraction plays an important role in a wide range of applications. Traditional line segment extraction algorithms focus on the accuracy and efficiency, without considering the integrity. Serious line segmentation fracture proble...
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Zhongguang Fu, Ke Lu and Yiming Zhu
As an important solution to issues regarding peak load and renewable energy resources on grids, large-scale compressed air energy storage (CAES) power generation technology has recently become a popular research topic in the area of large-scale industria...
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Climent Molins, Pau Trubat, Xavi Gironella and Alexis Campos
One of the main aspects when testing floating offshore platforms is the scaled mooring system, particularly with the increased depths where such platforms are intended. The paper proposes the use of truncated mooring systems to emulate the real mooring s...
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Binbin Zhou, Suzhen Feng, Zifan Xu, Yan Jiang, Youxiang Wang, Kai Chen and Jinwen Wang
A monthly hydropower scheduling determines the monthly flows, storage, and power generation of each reservoir/hydropower plant over a planning horizon to maximize the total revenue or minimize the total operational cost. The problem is typically a comple...
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Julien Lavandier, Arianit Islami, Daniel Delahaye, Supatcha Chaimatanan and Amir Abecassis
This paper presents a methodology to minimize the airspace congestion of aircraft trajectories based on slot allocation techniques. The traffic assignment problem is modeled as a combinatorial optimization problem for which a selective simulated annealin...
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