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AIAA Aviation Forum

作者： Chu, Sheng Townsend, Scott Featherston, Carol Kim, H. Alicia Cardiff Univ Sch Engn Cardiff CF24 3AA Wales Univ Calif San Diego Struct Engn Dept La Jolla CA 92093 USA

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

Simultaneous layout and topology optimization of stiffened panels is investigated in this paper by a new level-set-based method. Specifically, plate elements are used to construct a stiffened panel structure. The level set method is then used to manipulate the stiffener layout, with curved members allowed. A free-form mesh deformation method with a control mesh is utilized to adjust the finite element mesh. The level set method is also used to optimize the internal topologies of the stiffeners. Both mass minimization with buckling constraint and critical buckling load factor maximization with mass constraint are investigated. A semi-analytical sensitivity analysis is performed, and the optimization algorithm is outlined. For the buckling-constrained problem, the p-norm function is used to aggregate many buckling modes and a gradient-based optimizer is used with an adaptive scaling method to enforce an exact control of the buckling limit. Numerical investigations demonstrate and validate the proposed method.

关键词： Topology optimization Sensitivity Analysis optimization algorithm Finite Element Analysis Poisson's Ratio Gradient Free optimization Finite Difference Method Particle Swarm optimization Fast Marching Method Structural Engineering

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Review of optimization-Based Feature Selection algorithms on Healthcare Dataset 2nd

Review of Optimization-Based Feature Selection Algorithms on...

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2nd International Conference on Computing, Communications and Data Engineering (CCODE)

作者： Manonmani, M. Balakrishnan, Sarojini Avinashilingam Inst Home Sci & Higher Educ Women Dept Comp Sci Coimbatore Tamil Nadu India

ISBN: (纸本)9789811501357;9789811501340

Feature selection is an important function in classification and prediction technique, especially in medical data mining. It is embedded with the task of selecting a subset of relevant features that can be used in constructing a model. optimization algorithms play a significant role in medical data mining especially in diagnosing chronic disease because it offers good efficiency in less computational cost and time. Also, the classification algorithms yield better results when the feature selection algorithm is based on an objective function. This paper aims to provide a review of optimized feature selection algorithms and an overview of the proposed improved teaching learning based optimization algorithm (ITLBO) for classification and prediction of chronic kidney disease (CKD). The proposed algorithm aims to reduce the number of features required for diagnosing the CKD with an objective function that optimizes the features and selects them based on the updated weight of the fitness function.

关键词： optimization algorithm Objective function Chronic kidney disease (CKD) Improved teacher-learner-based optimization algorithm (ITLBO) Fitness function

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System Architecture Design Space Exploration: An Approach to Modeling and optimization

System Architecture Design Space Exploration: An Approach to...

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AIAA Aviation Forum

作者： Bussemaker, J. H. Ciampa, P. D. Nagel, B. DLR German Aerosp Ctr Inst Syst Architectures Aeronaut Hamburg Germany

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

Systematic modeling of architecture design spaces is needed when architecting complex systems, to support experts in making less biased decisions, and to formulate the optimization problem needed to explore the large combinatorial design space. Existing methods do not offer enough compatibility with the Model-Based Systems Engineering (MBSE) approaches, cannot model all needed design scenarios, or are not flexible enough when it comes to architecture evaluation. A new method is presented that provides a semantic representation of the architecture design space, modeled as the Architecture Design Space Graph (ADSG). The ADSG represents three types of architectural decisions: function-component mapping, component characterization, and component connection. The ADSG is constructed from a design space definition, and discrete architectural decisions are automatically inserted according to specified rules. Once decisions and metrics have been defined, the hierarchical, mixed-integer, multi-objective optimization problem can be formulated: decisions are mapped to design variables, and performance metrics are mapped to objectives or constraints. An application of the method to the Apollo mission architecting problem is presented.

关键词： Space Exploration Model Based System Engineering Apollo Missions Earth Orbit Rendezvous optimization algorithm Crew Member Earth Centrifugal Pump Turbofan Multi Objective Evolutionary algorithms

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Efficient Multi-Information Source Multi-Objective Bayesian optimization

Efficient Multi-Information Source Multi-Objective Bayesian ...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Khatamsaz, Danial Peddareddygari, Lalith Friedman, Samuel Allaire, Douglas Texas A&M Univ J Mike Walker 66 Dept Mech Engn College Stn TX 77843 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Multi-objective optimization is often a difficult task owing to the need to balance competing objectives. A typical approach to handling this is to estimate a Pareto frontier in objective space by identifying non-dominated points. This task is typically computationally demanding owing to the need to incorporate information of high enough fidelity to be trusted in design and decision-making processes. In this work, we present a multi-information source framework for enabling efficient multi-objective optimization. The framework allows for the exploitation of all available information and considers both potential improvement and cost. The framework includes ingredients of model fusion, expected hypervolume improvement, and intermediate Gaussian process surrogates. The approach is demonstrated on two test problems and aerostructural wing design problem.

关键词： Bayesian optimization Cumulative Distribution Function Pareto Frontier Surrogate Model optimization algorithm Aerodynamic Performance Finite Element Analysis Bréguet Range Equation Aircraft Wing Design Open Source Software

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Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets

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NEURAL COMPUTING & APPLICATIONS 2020年第17期32卷 14047-14067页

作者： Harandizadeh, Hooman Armaghani, Danial Jahed Mohamad, Edy Tonnizam Shahid Bahonar Univ Kerman Dept Civil Engn Fac Engn Pajoohesh SqImam Khomeni HighwayPOB 76169133 Kerman Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Univ Teknol Malaysia Sch Civil Engn Ctr Trop Geoengn GEOTROPIK Fac Engn Johor Baharu 81310 Malaysia

The tensile strength (TS) of the rock is one the most key parameters in designing process of foundations and tunnels structures. However, direct techniques for TS determination (laboratory investigations) are not efficient with respect to cost and time. This investigation attempts to develop an innovative hybrid intelligent model, i.e. fuzzy-group method of data handling (GMDH) optimized by the gravitational search algorithm (GSA), fuzzy-GMDH-GSA, for prediction of the rock TS. To establish a database, the rock samples collected from a tunnel site were evaluated in the laboratory and a database (with the Schmidt hammer test, dry density test, and point load test as inputs and Brazilian tensile strength, BTS, as output) was prepared for modelling. Then, a fuzzy-GMDH-GSA model was developed to predict BTS of the rock considering the most influential of this predictive model. In addition, a fuzzy model as well as a GMDH model were constructed to predict BTS for comparison purposes. The performances of the proposed predictive models were evaluated by comparing the values of several statistical metrics such as correlation coefficient (R). R values of 0.90, 0.86, and 0.86 were obtained for testing datasets of fuzzy-GMDH-GSA, GMDH, and fuzzy models, respectively, which show that the fuzzy-GMDH-GSA predictive model is able to deliver greater prediction performance compared to other constructed models. The results confirmed the effective role of the GSA, as a powerful optimization algorithm in efficiency of hybrid fuzzy-GMDH-GSA model. Moreover, results of sensitivity analysis showed that the point load index is the most effective input on output of this study.

关键词： Tensile strength Gravitational search algorithm optimization algorithm Group method of data handling Fuzzy system

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Solving The Printed Circuit Board Drilling Problem By Ant Colony optimization algorithm

Solving The Printed Circuit Board Drilling Problem By Ant Co...

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World Congress on Engineering and Computer Science

作者： Eldos, Taisir Kanan, Aws Aljumah, Abdullah Salman Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Engn Al Kharj 11942 Saudi Arabia

ISBN: (纸本)9789881925237

Printed Circuit Board (PCB) manufacturing depends on the holes drilling time, which is a function of the number of holes and the order in which they are drilled. A typical PCB may have hundreds of holes and optimizing the time to complete the drilling plays a role in the production rate. At an early stage of the manufacturing process, a numerically controlled drill has to move its bit over the holes one by one and must complete the job in minimal time. The order by which the holes are visited is of great significance in this case. Solving the TSP leads to minimizing the time to drill the holes on a PCB. Finding an optimal solution to the TSP maybe prohibitively large as the number of possibilities to evaluate in an exact search is (n-1)!/2 for n-hole PCB. There exist too many algorithms to solve the TSP in an engineering sense;semi-optimal solution, with good quality and cost tradeoff. Starting with Greedy algorithm which delivers a fast solution at the risk of being low in quality, to the evolutionary algorithms like Genetic algorithms, Simulated Annealing algorithms, Ant Colony, Swarm Particle optimization, and others which promise better solutions at the price of more search time. We propose an Ant Colony optimization (ACO) algorithm with problem-specific heuristics like making use of the dispersed locales, to guide the search for the next move. Hence, making smarter balance between the exploration and exploitation leading to better quality for the same cost or less cost for the same quality. This will also offer a better way of problem partitioning which leads to better parallelization when more processing power is to be used to deliver the solution even faster.

关键词： Ant Colony optimization algorithm Printed Circuits Board Drilling Traveling Salesman

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An optimal membership function based Fuzzy-PI^λ stabilizer design to suppress low frequency oscillation in synchronous generator

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RESULTS IN CONTROL AND optimization 2024年 14卷

作者： Acharya, Debasis Das, Dushmanta Kumar Natl Inst Technol Nagaland Dept Elect & Elect Engn Dimapur 797103 India

An optimal membership function-based fractional order fuzzy proportional integral (Fuzzy-PI lambda) power system stabilizer (PSS) is designed to suppress low frequency oscillation (LFO) in a synchronous generator connected to an infinite bus power system. Speed deviation and the change in speed deviation of synchronous generator due to LFO are taken as inputs of the fuzzy controller, and the parameters of a fractional-order PI controller (proportional gain, integral gain, and the fractional term in integral control action) are taken as fuzzy output variables. The range, scaling, and shape of the membership functions of the fuzzy controller are optimized such that it can tune the fractional order proportional integral (PI lambda) stabilizer under any disturbance or parametric uncertainty in the synchronous generator. The advantage of the proposed stabilizer is that it can handle a wide range of disturbances and adjust the gains of the control parameters for the system. A nonlinear dynamic acceleration coefficient-based class-topper optimization is applied to optimize the fuzzy settings. The robustness and sensitivity of the proposed optimal Fuzzy-PI lambda stabilizer are verified under different scenarios for the synchronous generator and compared with available results.

关键词： Fractional order controller Synchronous generator Fuzzy controller optimization algorithm Power system stabilizer

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Design and Implementation of Object Tracking System Based Mean-Shift with Locust Search optimization on Raspberry Pi 6

Design and Implementation of Object Tracking System Based Me...

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6th International Conference on Information Technology Systems and Innovation (ICITSI)

作者： Hardini, Inkreswari Retno Bandung, Yoanes Inst Teknol Bandung Sch Elect Engn & Informat Bandung Indonesia

ISBN: (纸本)9781728181967

Having an optimal object tracking system is an advantage especially in everyday use. One of the example is use in victim localization. The optimal object tracking system as mentioned earlier means a system that capable of reaching the convergence point of region of interest (ROI) with small number of iterations. The smaller the number of iterations needed for reaching the convergence point, the faster the system follows the object's movement. A system that is able to follow the movement of objects quickly, especially in accordance with case studies of victim localization, is certainly needed. In this paper, the object tracking system is built on Mean-Shift algorithm. Mean-Shift has a convergent search technique that requires a large number of iteration processes. In order to achieve an optimal object tracking system as the purpose of this paper, an optimized algorithm is carried out. Instead of shift the ROI point sequentially until reach the optimum point as done in Mean-Shift, the optimization algorithm will search the ROI optimum point randomly with a movement that pays attention to the previous optimum point. optimization algorithm used in this paper is Locust Search algorithm. Object interest being tracked in this paper is human face. Object tracking system will be deployed in Raspberry Pi 3 Model B+ because of its characteristics that are suitable to be implemented in this paper's case, in form of prototype.

关键词： Locust Search optimization algorithm Mean-Shift region of interest tracking object Raspberry Pi

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A patient-specific modelling method of blood circulatory system for the numerical simulation of enhanced external counterpulsation

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JOURNAL OF BIOMECHANICS 2020年 111卷 110002-110002页

作者： Li, Bao Wang, Hui Li, Gaoyang Liu, Jian Zhang, Zhe Gu, Kaiyun Yang, Haisheng Qiao, Aike Du, Jianhang Liu, Youjun Beijing Univ Technol Coll Life Sci & Bioengn Dept Biomed Engn Beijing Peoples R China Sun Yat Sen Univ Affiliated Hosp 8 Shenzhen Guangdong Peoples R China Tohoku Univ Inst Fluid Sci Sendai Miyagi Japan Peking Univ Peoples Hosp Beijing Peoples R China Peking Univ Third Hosp Beijing Peoples R China Beijing Univ Technol 100 Pingleyuan Beijing 100124 Peoples R China

Lumped parameter model (LPM) is a common numerical model for hemodynamic simulation of human's blood circulatory system. The numerical simulation of enhanced external counterpulsation (EECP) is a typical biomechanical simulation process based on the LPM of blood circulatory system. In order to simulate patient-specific hemodynamic effects of EECP and develop best treatment strategy for each individual, this study developed an optimization algorithm to individualize LPM elements. Physiological data from 30 volunteers including approximate aortic pressure, cardiac output, ankle pressure and carotid artery flow were clinically collected as optimization objectives. A closed-loop LPM was established for the simulation of blood circulatory system. Aiming at clinical data, a sensitivity analysis for each element was conducted to identify the significant ones. We improved the traditional simulated annealing algorithm to iteratively optimize the sensitive elements. To verify the accuracy of the patient-specific model, 30 samples of simulated data were compared with clinical measurements. In addition, an EECP experiment was conducted on a volunteer to verify the applicability of the optimized model for the simulation of EECP. For these 30 samples, the optimization results show a slight difference between clinical data and simulated data. The average relative root mean square error is lower than 5%. For the subject of EECP experiment, the relative error of hemodynamic responses during EECP is lower than 10%. This slight error demonstrated a good state of optimization. The optimized modeling algorithm can effectively individualize the LPM for blood circulatory system, which is significant to the numerical simulation of patient-specific hemodynamics. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Patient-specific modelling Blood circulatory system Enhanced external counterpulsation optimization algorithm Lumped parameter model

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Investigation of Fast, Secure and Reliable Network Restoration after Blackouts

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ENERGIES 2020年第11期13卷 3019页

作者： Makhsos, Elmira Torabi Guo, Yi Gawlik, Wolfgang Cox, Benjamin Hinkel, Philipp Zugck, Marian Wellssow, Wolfram Schmaranz, Robert Traxler, Ewald Fiedler, Leopold Tech Univ Wien Inst Energy Syst & Elect Drives A-1040 Vienna Austria Tech Univ Kaiserslautern Div Energy Syst & Energy Management D-67663 Kaiserslautern Germany KNG Karnten Netz GmbH A-9020 Klagenfurt Am Worthersee Austria Netz Oberosterreich GmbH A-4030 Linz Austria

Integrating distributed generation into power grids creates various technical challenges for network operation. Volatility of renewable energy resources may increase the probability of blackouts. In order to restore networks fast, securely and reliably after blackouts, within the research project RestoreGrid4RES's network restoration strategies, the related issues caused by distributed generation are investigated and novel methods to face those challenges are developed. This paper focuses on (i) algorithms to identify possible restoration paths, (ii) key performance indicators for the assessment of grid restoration options and (iii) an evaluation of the results for network restoration strategies.

关键词： power system restoration distributed generation optimization algorithm key performance indicator

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