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Usage of coot optimization-based random forests analysis for determining the shallow foundation settlement

GEOMECHANICS AND ENGINEERING 2023年第3期32卷 271-291页

作者： Han, Yi Jiang, Xingliang Wang, Ye Wang, Hui Anhui Sci & Technol Univ Sch Architecture Bengbu 233100 Anhui Peoples R China CCCC Water Transportat Consultants Co Ltd Beijing 100007 Peoples R China Tongji Univ Dept Civil Engn Shanghai 200092 Peoples R China

Settlement estimation in cohesion materials is a crucial topic to tackle because of the complexity of the cohesion soil texture, which could be solved roughly by substituted solutions. The goal of this research was to implement recently developed machine learning features as effective methods to predict settlement (S-m) of shallow foundations over cohesion soil properties. These models include hybridized support vector regression (SVR), random forests (RF), and coot optimization algorithm (COM), and black widow optimization algorithm (BWOA). The results indicate that all created systems accurately simulated theS(m) with anR(2) of better than 0.979 and 0.9765 for the train and test data phases, respectively. This indicates extraordinary efficiency and a good correlation between the experimental and simulatedS(m). The model's results outperformed those ofANFIS -PSO andCOM -RF findings were much outstanding to those of the literature. By analyzing established designs utilizing different analysis aspects, such as various error criteria, Taylor diagrams, uncertainty analyses, and error distribution, it was feasible to arrive at the final result that the recommendedCOM-RF. was the outperformed approach in the forecasting process of S-m of shallow foundation, while other techniques were also reliable.

关键词： forecasting optimization algorithms random forests settlement shallow foundation support vector regression

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A response surface for structural optimization

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JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING-TRANSACTIONS OF THE ASME 1997年第3期119卷 196-202页

作者： Rule, WK Department of Aerospace Engineering and Mechanics University of Alabama Box 870280 Tuscaloosa AL 35487-0280

Structural optimization is computationally intensive. Typically, a finite element model must be solved repeatedly during the process. Accordingly, various approximation schemes have been developed to reduce the cost of solves or to eliminate solves entirely during portions of the optimization process. In this paper, a new type of approximation or interpolation function in the form of a global response surface is described. With this function, approximations can be made with a limited amount of calibrating data points. The response surface requires no matrix inversions for coefficient evaluation, and therefore it is relatively efficient and stable. The new response surface is used in conjunction with a modified zero-order Powell search algorithm. The effectiveness of the response surface is evaluated for a reasonably realistic offshore structure application. This planar truss structure is subjected to a deck load, a lateral wave load (considered quasi-static), and member self-weight loads. The objective for this test case was to minimize the structural cost. Nodal coordinates were considered to be the explicit design variables. Response surface results are compared with those obtained by the common optimization algorithms: steepest descent, conjugate directions, and a conventional implementation of Powell's method. The response surface appears to be effective for the test case considered.

关键词： Structural optimization Weight (Mass) optimization Function approximation Waves Interpolation Trusses (Building) Stress Design Offshore structures optimization algorithms Approximation Response surface methodology Finite element model algorithms

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optimization of injection moulding conditions with user-definable objective functions based on a genetic algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2004年第7期42卷 1365-1390页

作者： Deng, YM Lam, YC Britton, GA Nanyang Technol Univ Sch Mech & Prod Engn Singapore 639798 Singapore Nanyang Technol Univ Singapore MIT Alliance Singapore 639798 Singapore

This paper applies a Genetic Algorithm (GA) method to optimize injection moulding conditions, such as melt temperature, mould temperature and injection time. A GA is very suitable for moulding conditions optimization where complex patterns of local minima are possible. Existing work in the literature has limited versatility because the optimization algorithm is hard-wired with specific objective function. However, for most of the practical applications, the appropriateness of optimization objective functions depends on each specific moulding problem. The paper develops a multi-objective GA optimization strategy, where the objective functions may be defined by the designers, including using different criteria and/or weights. For parts with general quality requirements, an objective function is also recommended with some quality measuring criteria, which are either more accurately represented or cover more moulding defects than those from existing simulation-based optimization approaches. The paper also elaborates on the effective GA attributes suited to moulding conditions optimization, such as population size, crossover rate and mutation rate. A case study demonstrates the effectiveness of the proposed approach and algorithm. The optimization results are compared with those from an exhaustive search method to determine the algorithm's accuracy in finding global optimum. It is found to be favourable.

关键词： Search methods quality requirements Injection molding Gallium Objective function global optimum Mutation Rate optimization algorithms Designers Genetic Algorithm (GA)

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Predicting the seismic response of the short structures by considering the Whale optimization Algorithm

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ENERGY REPORTS 2021年 7卷 4071-4084页

作者： Liu, Zhe Zhang, Lei Li, Jiajing Mamluki, Mina Guangdong Univ Petrochem Technol Sch Petr Engn Maoming 525000 Guangdong Peoples R China Chongqing Univ Sci & Technol Sch Petr Engn Chongqing 401331 Peoples R China Univ Mohaghegh Ardabili Ardebil Iran

This paper presents a newly improved version of the Whale optimization Algorithm based on quantum theory by optimizing the Artificial Neural Network for the ground response approximation problems in short buildings. Based on the literature, Artificial Neural Network does not provide a proper response of ground surface prediction in seismic load applications. Hence, the main idea of this study is to assess the utilization of the whale optimization algorithm for calculating the columns' horizontal deflection in the short structure under notable considered seismic loading. The input database utilized in this paper is the Chi-Chi earthquake that is occurred in 1999 in Taiwan. To provide the train and test databases for the artificial neural network algorithm and whale optimization algorithm, Finite Element models are used. The inputs contain the Chi-Chi earthquake's dynamic time, soil elastic modulus, dilation angle, Poisson's ratio, unit weight, friction angle, bending stiffness and axial stiffness. Also the columns' horizontal deflection in their highest level is considered as the output. The results showed the higher reliability of the proposed WOA in calculating the ground response and the column's horizontal deflection in short buildings subjected to the earthquake loading. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Short structures Earthquake ANN WOA-ANN PSO-ANN optimization algorithms

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An Efficient Algorithm for Power Flow optimization in PV Inverters Systems

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2020年第12-13期48卷 1362-1377页

作者： Alsafasfeh, Qais Saraereh, Omar A. Alsafasfeh, Moath Maqableh, Ayman Khan, Imran Choi, Bong Jun Tafila Tech Univ Dept Elect Power & Mechatron Tafila Jordan Hashemite Univ Dept Elect Engn Zarqa Jordan Al Hussein Bin Talal Univ Comp Engn Dept Maan Jordan Luminus Tech Univ Coll Electromech Engn Dept Amman Jordan Univ Engn & Technol Dept Elect Engn Peshawar Pakistan Soongsil Univ Sch Comp Sci & Engn Seoul South Korea

Aiming at the problem of optimal power flow in photovoltaic (PV) Inverters, this paper proposes a real-time algorithm for optimal power flow (OPF) in distributed PV inverters with sophisticated communication technologies and measurement. A higher accuracy and computation speed are obtained by proposing a linear, time-varying optimization method, and the objective function and constraints were linearized using the Taylor expansion under the premise of small variable changes during the short control period. By comparing with the constant linearization method, the proposed time-varying linear approximation method is more accurate and efficient. The problems faced by the centralized calculation is overcome by proposing a distributed control method which utilizes the alternating direction method of multipliers. The proposed distributed control method enables the distributed PV inverters to optimize their own power setpoints with only a little information required from neighbor nodes. Case study results demonstrate that the proposed algorithm makes the distributed PV Inverters more efficient and economical than the other methods.

关键词： photovoltaic renewable energy optimal power flow power grid optimization PV inverters time-varying optimization alternation direction multiplier distributed generation solar power systems optimization algorithms

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Design and optimization of 3-D compact stripline and microstrip Bluetooth/WLAN balun architectures using the design of experiments technique

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2005年第5期53卷 1805-1812页

作者： Staiculescu, D Bushyager, N Obatoyinbo, A Martin, LJ Tentzeris, MM Georgia Inst Technol Sch Elect & Comp Engn Georgia Elect Design Ctr Atlanta GA 30332 USA Hughes Res Labs Malibu CA 90265 USA Motorola Inc Plantation FL 33322 USA

In this paper, various architectures of three-dimensional compact microwave balanced to unbalanced (balun) transformers for Bluetooth/WiFi antenna applications are successfully designed and optimized using the design of experiments (DOE) approach. Two different multilayer topologies, one microstrip and one stripline, are investigated on low temperature co-fired ceramic (LTCC) substrate. The design goals for both baluns are perfectly balanced outputs from 2 to 3 GHz and a resonant frequency of exactly 2.4 GHz. It is demonstrated, using only eight simulations, that perfectly balanced outputs are not possible under the given conditions in the case of the microstrip balun. Nevertheless, the stripline balun can be optimized due to its almost symmetrical structure, and both simulations and measurement results verify the conclusions. The DOE method is very simple to implement and gives a clear understanding of the system behavior at the beginning of the design process, reducing the amount of work required for achieving the design goals by orders of magnitude compared to the widely used trial-and-error approach. The matching and unique measurement issues regarding the calibration, placement of probes and the de-embedding of the microstrip to coplanar waveguide transitions are discussed in detail for the optimized stripline balun. This technique can be easily applied to the fast and efficient optimization of complicated radiation structures, such as reconfigurable or multilayer mutliband antenna arrays.

关键词： coplanar waveguide (CPW) lines design of experiments (DOE) low temperature co-fired ceramic (LTCC) microstrip lines multilayer modules optimization algorithms three-dimensional (3-D) balun WiFi/Bluetooth antennas

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Machine learning and optimization for production rescheduling in Industry 4.0

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第9-10期110卷 2445-2463页

作者： Li, Yuanyuan Carabelli, Stefano Fadda, Edoardo Manerba, Daniele Tadei, Roberto Terzo, Olivier LINKS Fdn Via Pier Carlo Boggio 61 I-10138 Turin Italy Politecn Torino Dept Control & Comp Engn Corso Duca Abruzzi 24 I-10129 Turin Italy Univ Brescia Dept Informat Engn Via Branze 38 I-25123 Brescia Italy Politecn Torino ICT City Logist & Enterprises Lab Corso Duca Abruzzi 24 I-10129 Turin Italy

Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under uncertainty, it is still a complicated task to decide whether a rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a tradeoff between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates machine learning (ML) techniques and optimization algorithms. To prove the effectiveness, we first model a flexible job-shop scheduling problem with sequence-dependent setup and limited dual resources (FJSP) inspired by an industrial application. Then, we solve the scheduling problem through a hybrid metaheuristic approach. We train the ML classification model for identifying rescheduling patterns. Finally, we compare its rescheduling performance with periodical rescheduling approaches. Through observing the simulation results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and scheduling delays. The main contributions of the work are the formalization of the FJSP problem, the development of ad hoc solution methods, and the proposal/validation of an innovative ML and optimization-based framework for supporting rescheduling decisions.

关键词： Industry 4 0 Flexible job-shop scheduling Rescheduling Machine learning classification optimization algorithms Real-time data analysis

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optimization OF RING MAGNET GEOMETRY FOR FIELD HARMONIC CONTROL IN ELECTRIC MOTORS

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IEEE TRANSACTIONS ON MAGNETICS 1992年第6期28卷 3380-3386页

作者： RAHMAN, BS LIEU, DK UNIV CALIF BERKELEY DEPT MECH ENGNBERKELEYCA 94720

The magnetic forcing function created by the rotating magnetic poles of an electric machine can induce vibration in the motor and surrounding structure, particularly if any of the function harmonics match an eigenfrequency of the structure. We present an optimization algorithm for shaping a multi-pole, continuous magnet for elimination of one or more harmonics. The magnet is modeled as a continuous ring portion and an optimized section, modeled as a series of thin layers, at each pole. The analytical model investigated consisted of a radially magnetized 4-pole motor with a solid ring stator. The algorithm successfully shaped the poles to eliminate selectively the 8th, 12th, or 16th harmonic, at the expense of increasing the amplitudes of the other harmonics. The solutions were not unique. A tradeoff in effectiveness existed between the minimum thickness of the continuous ring and the minimum sector angle of the outer pole layer. For thin rings and small outer layer sector angles, an optimum pole shape was readily found. However, if one parameter was too large, then the other had to be decreased for an effective solution to exist.

关键词： THIN PLATES induction motors ring magnet one-parameter rings HARMONIC CONTROL harmonics pole shape Pole Annulus Ring device Electric motors optimization algorithms MAGNETS

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Calibration and optimization of the Pumping and Disinfection of a Real Water Supply System

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT-ASCE 2010年第4期136卷 493-501页

作者： Gibbs, Matthew S. Dandy, Graeme C. Maier, Holger R. Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia

Maintaining a disinfectant residual in water distribution systems (WDSs) is generally considered paramount to ensuring a safe drinking water supply. This objective can be assisted by the use of booster stations to increase disinfectant concentrations throughout the network. However, identifying the appropriate dose at each station is an optimization problem. The aim is to minimize the total mass of disinfectant dosed and reduce the cost of disinfection along with potential taste, odor, or by-product problems, while maintaining a certain minimum residual in the network. The residual present in the water at any location is not only dependent on the amount of disinfectant added to the water, but also the hydraulics of the system and the resulting detention times. A number of previous studies have tackled this optimization problem, however, a review of current literature suggests that in many cases the hydraulics of the system have been held constant, or the WDSs considered were hypothetical systems with relatively few constraints. This study considers the booster disinfection dosing problem, including daily pump scheduling, for a real system in Sydney, Australia. Before the system can be optimized, a representative model is required to ensure useful results, and the many constraints on the daily operation system must be accounted for in the fitness function considered. The results from the optimization study indicate it is necessary to consider the hydraulics as well as the dosing regime in the optimization process, as cycling reservoir levels minimizes detention times, and hence, disinfectant residuals are maintained at the extremities of the network. Also, significant energy cost savings of up to 30% can be made by scheduling the pumping in the system in line with the off-peak electricity costs.

关键词： Genetic algorithms Evolutionary computation optimization algorithms Water distribution systems Pipe networks Water quality

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Metaheuristics for Solving Global and Engineering optimization Problems: Review, Applications, Open Issues and Challenges

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2024年第8期31卷 4485-4519页

作者： Houssein, Essam H. Saeed, Mahmoud Khalaf Hu, Gang Al-Sayed, Mustafa M. Minia Univ Fac Comp & Informat Al Minya Egypt Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China

The greatest and fastest advances in the computing world today require researchers to develop new problem-solving techniques capable of providing an optimal global solution considering a set of aspects and restrictions. Due to the superiority of the metaheuristic algorithms (MAs) in solving different classes of problems and providing promising results, MAs need to be studied. Numerous studies of MAs algorithms in different fields exist, but in this study, a comprehensive review of MAs, its nature, types, applications, and open issues are introduced in detail. Specifically, we introduce the metaheuristics' advantages over other techniques. To obtain an entire view about MAs, different classifications based on different aspects (i.e., inspiration source, number of search agents, the updating mechanisms followed by search agents in updating their positions, and the number of primary parameters of the algorithms) are presented in detail, along with the optimization problems including both structure and different types. The application area occupies a lot of research, so in this study, the most widely used applications of MAs are presented. Finally, a great effort of this research is directed to discuss the different open issues and challenges of MAs, which help upcoming researchers to know the future directions of this active field. Overall, this study helps existing researchers understand the basic information of the metaheuristic field in addition to directing newcomers to the active areas and problems that need to be addressed in the future.

关键词： optimization algorithms

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