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New atmospheric data model for constant altitude accelerated flight performance prediction calculations and flight trajectory optimization algorithms

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2021年第4期235卷 405-426页

作者： Dancila, Radu, I Botez, Ruxandra M. Univ Quebec Ecole Technol Super Lab Res Act Control Avion & Aeroservoelast LARCAS Montreal PQ Canada

This article presents a new method for storing and computing the atmospheric data used in time-critical flight trajectory performance prediction calculations, such as flight performance prediction calculations in flight management systems and/or flight trajectory optimization, of constant altitude cruise segments. The proposed model is constructed based on the forecast data provided by Meteorological Service Agencies, in a GRIB2 data file format, and the set of waypoints that define the lateral component of the evaluated flight profile(s). The atmospheric data model can be constructed/updated in the background or off-line, when new atmospheric prediction data are available, and subsequently used in the flight performance computations. The results obtained using the proposed model show that, on average, the atmospheric parameter values are computed six times faster than through 4D linear interpolations, while yielding identical results (value differences of the order of 10e-14). When used in flight trajectory performance calculations, the obtained results show that the proposed model conducts to significant computation time improvements. The proposed model can be extended to define the atmospheric data for a set of cruise levels (usually multiple of 1000 ft).

关键词： Four-dimensional interpolations techniques aircraft performances atmospheric data model flight trajectory optimization algorithms

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A UNIFORM ENHANCEMENT APPROACH FOR optimization algorithms: SMOOTHING FUNCTION METHOD

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2010年第7期24卷 1111-1131页

作者： Wang, Yuping Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

In this paper, we propose a uniform enhancement approach called smoothing function method, which can cooperate any optimization algorithm and improve its performance. The method has two phases. In the first phase, a smoothing function is constructed by using a properly truncated Fourier series. It can preserve the overall shape of the original objective function but eliminate many of its local optimal points, thus it can well approach the objective function. Then, the optimal solution of the smoothing function is searched by an optimization algorithm (e. g. traditional algorithm or evolutionary algorithm) so that the search becomes much easier. In the second phase, we switch to optimize the original function for some iterations by using the best solution(s) obtained in phase 1 as an initial point (population). Thereafter, the smoothing function is updated in order to approximate the original function more accurately. These two phases are repeated until the best solutions obtained in several successively second phases cannot be improved obviously. In this manner, any optimization algorithm will become much easier in searching optimal solution. Finally, we use the proposed approach to enhance two typical optimization algorithms: Powell direct algorithm and a simple genetic algorithm. The simulation results on ten challenging benchmarks indicate the proposed approach can effectively improve the performance of these two algorithms.

关键词： Smoothing function method uniform enhancement approach optimization algorithms

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Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential

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ENGINEERING WITH COMPUTERS 2022年第4期38卷 3611-3623页

作者： Cai, Mingxiang Hocine, Ouaer Mohammed, Ahmed Salih Chen, Xiaoling Amar, Menad Nait Hasanipanah, Mahdi Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Remote Sensing Wuhan 430079 Peoples R China Univ MHamed Bougara Boumerdes Fac Hydrocarbures & Chim Lab Genie Phys Hydrocarbures Ave Independance Boumerdes 35000 Algeria Univ Sulaimani Civil Engn Dept Coll Engn Sulaymaniyah Kurdistan Regio Iraq Dept Etud Thermodynam Div Lab Boumerdes Algeria Univ Kashan Dept Min Engn Kashan Iraq

Liquefaction has caused many catastrophes during earthquakes in the past. When an earthquake is occurring, saturated granular soils may be subjected to the liquefaction phenomenon that can result in significant hazards. Therefore, a valid and reliable prediction of soil liquefaction potential is of high importance, especially when designing civil engineering projects. This study developed the least squares support vector machine (LSSVM) and radial basis function neural network (RBFNN) in combination with the optimization algorithms, i.e., the grey wolves optimization (GWO), differential evolution (DE), and genetic algorithm (GA) to predict the soil liquefaction potential. Afterwards, statistical scores such as root mean square error were applied to evaluate the developed models. The computational results showed that the proposed RBFNN-GWO and LSSVM-GWO, with Coefficient of Determination (R-2) = 1 and Root Mean Square Error (RMSE) = 0, produced better results than other models proposed previously in the literature for the prediction of the soil liquefaction potential. It is an efficient and effective alternative for the soil liquefaction potential prediction. Furthermore, the results of this study confirmed the effectiveness of the GWO algorithm in training the RBFNN and LSSVM models. According to sensitivity analysis results, the cyclic stress ratio was also found as the most effective parameter on the soil liquefaction in the studied case.

关键词： Soil liquefaction potential Radial basis function neural network Least squares support vector machine optimization algorithms

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An active control for hydrostatic journal bearing using optimization algorithms

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INDUSTRIAL LUBRICATION AND TRIBOLOGY 2021年第2期73卷 316-324页

作者： Ur Rehman, Waheed Wang, Xinhua Chen, Yingchun Yang, Xiaogao Ullah, Zia Cheng, Yiqi Kanwal, Marya Beijing Univ Technol Coll Mech Engn & Appl Elect Technol Beijing Peoples R China Hunan Univ Arts & Sci Coll Mech Engn Changde Peoples R China

Purpose - The purpose of this paper is to improve static/dynamic characteristics of active-controlled hydrostatic journal bearing by using fractional order control techniques and optimizing algorithms. Design/methodology/approach - Active lubrication has ability to overcome the unpredictable harsh environmental conditions which often lead to failure of capillary controlled traditional hydrostatic journal bearing. The research develops a mathematical model for a servo feedback-controlled hydrostatic journal bearing and dynamics of model is analyzed with different control techniques. The fractional-order PID control system is tuned by using particle swarm optimization and Nelder mead optimization techniques with the help of using multi-objective performance criteria. Findings - The results of the current research are compared with previously published theoretical and experimental results. The proposed servo-controlled active bearing system is studied under a number of different dynamic situations and constraints of variable spindle speed, external load, temperature changes (viscosity) and variable bearing clearance (oil film thickness). The simulation results show that the proposed system has better performance in terms of controllability, faster response, stability, high stiffness and strong resistance. Originality/value - This paper develops an accurate mathematical model for servo-controlled hydrostatic bearing with fractional order controller. The results are in excellent agreement with previously published literature.

关键词： optimization algorithms Fractional order control Hydrostatic journal bearings Servo control systems Fractional order control Hydrostatic journal bearings optimization algorithms Servo control systems

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Model specification searches using ant colony optimization algorithms

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STRUCTURAL EQUATION MODELING 2003年第1期10卷 154-164页

作者： Marcoulides, GA Drezner, Z Calif State Univ Fullerton Fullerton CA 92834 USA

Ant colony optimization is a recently proposed heuristic procedure inspired by the behavior of real ants. This article applies the procedure to model specification searches in structural equation modeling and reports the results. The results demonstrate the capabilities of ant colony optimization algorithms for conducting automated searches.

关键词： .conducting Model Specification Ant Colony optimization algorithms true model Spirtes specification search Glymour

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Modeling of aquifer vulnerability index using deep learning neural networks coupling with optimization algorithms

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第40期28卷 57030-57045页

作者： Elzain, Hussam Eldin Chung, Sang Yong Senapathi, Venkatramanan Sekar, Selvam Park, Namsik Mahmoud, Ahmed Abdulhamid Pukyong Natl Univ Dept Environm & Earth Sci Busan 48513 South Korea Alagappa Univ Dept Disaster Management Karaikkudi 630003 Tamil Nadu India VO Chidambaram Coll Dept Geol Thoothukudi 628008 India Dong A Univ Dept Civil Engn Busan 49315 South Korea King Fand Univ Petr & Minerals Coll Petr Engn & Geosci Dhahran 31261 Saudi Arabia

A reliable assessment of the aquifer contamination vulnerability is essential for the conservation and management of groundwater resources. In this study, a recent technique in artificial intelligence modeling and computational optimization algorithms have been adopted to enhance the groundwater contamination vulnerability assessment. The original DRASTIC model (ODM) suffers from the inherited subjectivity and a lack of robustness to assess the final aquifer vulnerability to nitrate contamination. To overcome the drawbacks of the ODM, and to maximize the accuracy of the final contamination vulnerability index, two levels of modeling strategy were proposed. The first modeling strategy used particle swarm optimization (PSO) and differential evolution (DE) algorithms to determine the effective weights of DRASTIC parameters and to produce new indices of ODVI-PSO and ODVI-DE based on the ODM formula. For strategy-2, a deep learning neural networks (DLNN) model used two indices resulting from strategy-1 as the input data. The adjusted vulnerability index in strategy-2 using the DLNN model showed more superior performance compared to the other index models when it was validated for nitrate values. Study results affirmed the capability of the DLNN model in strategy-2 to extract the further information from ODVI-PSO and ODVI-DE indices. This research concluded that strategy-2 provided higher accuracy for modeling the aquifer contamination vulnerability in the study area and established the efficient applicability for the aquifer contamination vulnerability modeling.

关键词： Aquifer contamination vulnerability Deep learning neural networks Modeling at two levels of strategy optimization algorithms

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A review on optimization algorithms and application to wind energy integration to grid

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2015年 48卷 214-227页

作者： Behera, Sasmita Sahoo, Subhrajit Pati, B. B. VSS Univ Technol Dept Elect & Elect Engn Burla 768018 Odisha India VSS Univ Technol Dept Elect Engn Burla 768018 Odisha India

In order to meet power needs, with concern for economics and environment, wind energy conversion is gradually gaining interest as a suitable source of renewable energy. To maximize the power extraction from the wind, optimization techniques are used at the various module of a wind farm starting from wind farm design for siting, sizing, optimal placement and sizing of distributed generation (DG) sources, generation scheduling, tuning of PID controller, control of wind energy conversion system (WECS) etc. This paper mainly focuses on the optimization algorithms (mostly the swarm based) in relation to integration of the wind farm with the grid. The paper here gives a precise idea about different optimization techniques, their advantage and disadvantage with respect to a wind farm. This review will enable the researchers to open the mind to explore possible applications in this field as well as beyond this area. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： optimization algorithms Wind farm design for sitting Optimal placement and sizing of DG Generation scheduling PID controller tuning Control of wind energy conversion system (WECS)

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A Tutorial on Multi-time Scale optimization Models and algorithms

arXiv

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arXiv 2025年

作者： Ramanujam, Asha Li, Can Davidson School of Chemical Engineering Purdue University 480 W.Stadium Ave West LafayetteIN47907 United States

Systems across different industries consist of interrelated processes and decisions in different time scales including long-time decisions and short-term decisions. To optimize such systems, the most effective approach is to formulate and solve multi-time scale optimization models that integrate various decision layers. In this tutorial, we provide an overview of multi-time scale optimization models and review the algorithms used to solve them. We also discuss the metric Value of the Multi-scale Model (VMM) introduced to quantify the benefits of using multi-time scale optimization models as opposed to sequentially solving optimization models from high-level to low-level. Finally, we present an illustrative example of a multi-time scale capacity expansion planning model and showcase how it can be solved using some of the algorithms (https: //***/li-group/***). This tutorial serves as both an introductory guide for beginners with no prior experience and a high-level overview of current algorithms for solving multi-time scale optimization models, catering to experts in process systems engineering. © 2025, CC BY.

关键词： optimization algorithms

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Best practices for comparing optimization algorithms

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optimization AND ENGINEERING 2017年第4期18卷 815-848页

作者： Beiranvand, Vahid Hare, Warren Lucet, Yves Univ British Columbia Dept Comp Sci Kelowna BC Canada Univ British Columbia Dept Math Kelowna BC Canada

Comparing, or benchmarking, of optimization algorithms is a complicated task that involves many subtle considerations to yield a fair and unbiased evaluation. In this paper, we systematically review the benchmarking process of optimization algorithms, and discuss the challenges of fair comparison. We provide suggestions for each step of the comparison process and highlight the pitfalls to avoid when evaluating the performance of optimization algorithms. We also discuss various methods of reporting the benchmarking results. Finally, some suggestions for future research are presented to improve the current benchmarking process.

关键词： Benchmarking Algorithm comparison Guidelines Performance Software Testing Metric Timing optimization algorithms

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Graph Theory and optimization algorithms Aided Adaptive Protection in Reconfigurable Microgrid

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2020年第1期15卷 421-431页

作者： Swathika, O. V. Gnana Hemamalini, S. VIT Univ Sch Elect Engn Chennai Tamil Nadu India

Microgrids interfaced with distributed generators facilitate decentralization of electric power. Bi-directional power flow due to multiple sources and dynamic behaviour of microgrids possess challenges to protection engineers. In this paper, an adaptive protection scheme for a central protection centre (CPC) in a microgrid is proposed. The key functions of the CPC are monitoring the microgrid, identification of fault if any, shortest path identification from a fault to the nearest operating source using Boruvka-Dijkstra graph theory algorithm. It also assigns adaptively the optimized values of time multiplier setting of relays in that path using genetic algorithm, which in turn aids in quick fault clearance. A hardware prototype is developed, tested and validated for a 7-bus microgrid network using Arduino ATmega 1280 for shortest path identification and optimized TMS value assignment for relays in that path using Raspberry Pi Model B+.

关键词： Graph theory optimization algorithms Adaptive protection Microgrid

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