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A spreadsheet-based tool for optimal design of reinforced concrete cantilever retaining walls

INNOVATIVE INFRASTRUCTURE SOLUTIONS 2022年第1期7卷 1-13页

作者： Shakeel, Mansoor Azam, Rizwan Riaz, Muhammad Rizwan Univ Engn & Technol Dept Civil Engn Lahore 54890 Pakistan

Optimization is the ultimate objective of engineers, through which they can design the most economical sections while having optimal performance. The ever-growing computational abilities and advent of improved optimization techniques have further eased its implementation. Despite these advancements, a review of literature has revealed that the research on optimization of reinforced concrete (RC) cantilever retaining walls is lacking compared to other RC members. The limited research reported so far on the optimal design of RC cantilever retaining walls has demonstrated the effectiveness of optimization techniques. However, there is a critical deficiency in its acceptance in the industry due to the absence of any optimization tool. In the current study, a spreadsheet-based tool is developed in which input parameters from a typical design can be entered to obtain an optimal solution for minimum cost design of RC cantilever retaining walls. The tool utilizes the metaheuristic technique of the evolutionary algorithm for optimization. To demonstrate the effectiveness of the developed tool, it is used to optimize the design of three RC cantilever retaining walls obtained from the literature. The results obtained from the spreadsheet-based tool demonstrated the ease and flexibility in its application, superior computational speeds, and cost saving of up to 30% for the considered case studies.

关键词： Optimization tool Reinforced concrete Cantilever retaining wall Optimization techniques Spreadsheet optimization Optimum design evolutionary algorithm

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Finding a generic fixed brake force distribution through optimizing hydraulic brake system parameters to prevent wheel lock

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WORLD JOURNAL OF ENGINEERING 2024年第6期21卷 1206-1218页

作者： Banik, Indranil Nandi, Arup Kumar Mondal, Bittagopal Acad Sci & Innovat Res Ghaziabad India CSIR Cent Mech Engn Res Inst Dept Intelligent Syst Engn Durgapur India CSIR Cent Mech Engn Res Inst Dept Adv Design & Anal Durgapur India

PurposeThe paper aims to identify a suitable generic brake force distribution ratio (beta) corresponding to optimal brake design attributes in a diminutive driving range, where road conditions do not exhibit excessive variations. This will intend for an appropriate allocation of brake force distribution (BFD) to provide dynamic stability to the vehicle during ***/methodology/approachTwo techniques are presented (with and without wheel slip) to satisfy both brake stability and performance while accommodating variations in load sharing and road friction coefficient. Based on parametric optimization of the design variables of hydraulic brake using evolutionary algorithm, taking into account both the laden and unladen circumstances simultaneously, this research develops an improved model for computing and simulating the BFD applied to commercial and passenger *** optimal parameter values defining the braking system have been identified, resulting in effective beta = 0.695 which enhances the brake forces at respective axles. Nominal slip of 3.42% is achieved with maximum deceleration of 5.72 m/s2 maintaining directional stability during braking. The results obtained from both the methodologies are juxtaposed and assessed governing the vehicle stability in straight line motion to prevent wheel ***/valueOptimization results establish the practicality, efficacy and applicability of the proposed approaches. The findings provide valuable insights for the design and optimization of hydraulic drum brake systems in modern automobiles, which can lead to safer and more efficient braking systems.

关键词： Brake force distribution Hydraulic brake system Wheel lock Wheel slip Brake stability Parametric optimization evolutionary algorithm

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PSO as Complex Network-Capturing the Inner Dynamics-Initial Study 2nd

PSO as Complex Network-Capturing the Inner Dynamics-Initial ...

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2nd International Afro-European Conference for Industrial Advancement (AECIA)

作者： Pluhacek, Michal Janostik, Jakub Senkerik, Roman Zelinka, Ivan Davendra, Donald Tomas Bata Univ Zlin Fac Appl Informat TG Masaryka 5555 Zlin 76001 Czech Republic VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci 17 Listopadu 15 Ostrava 70833 Czech Republic

ISBN: (纸本)9783319295046;9783319295039

This paper presents an initial proposal of methodology for converting the inner dynamics of PSO algorithm into complex network. The motivation is in the recent trend of adaptive methods for improving the performance of evolutionary computational techniques. It seems very likely that the complex network and its statistical characteristics can be used within those adaptive approaches. The methodology described in this paper manages to put significant amount of information about the inner dynamics of PSO algorithm into a complex network.

关键词： Particle Swarm Optimization PSO evolutionary algorithm Complex network

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Multi-objective Approach for Semi-Supervised Discriminant Analysis with Relative Distance

Multi-objective Approach for Semi-Supervised Discriminant An...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Sanodiya, Rakesh Kumar Saha, Sriparna Mathew, Jimson Thalakottur, Michelle Davies Aadya, Utkarshinee Indian Inst Technol Patna Dept Comp Sci & Engn Patna Bihar India MKSSS Cummins Coll Engineer Comp Engn Pune Maharashtra India Birla Inst Technol Mesra Math Ranchi Bihar India

ISBN: (纸本)9781728121536

Data such as videos, genetic information, etc. from real-world applications reside in a high dimensional space. Before performing classification, it is required to project the data from the high dimensional space to a lower dimensional space without losing too much information. Linear discriminant analysis (LDA) is one of the most widely used methods for dimensionality reduction, that maximizes the ratio of the between-class scatter and total data scatter in the projected space using the labeled information. However, in the real world scenario, labeled information is hardly ever available in large quantities, but an abundant amount of unlabeled data is available. In this paper, we propose a Semi-Supervised Discriminant Analysis method called SSDARD, which considers the unlabeled information in the form of a k-NN graph. Different from the existing semi-supervised dimensionality reduction algorithms, our algorithm is more consistent in propagating the label information from labeled data to unlabeled data due to the use of relative distance function instead of normal Euclidean distance function to generate the k-NN graph. To find an appropriate relative distance function, we use pairwise constraints generated from labeled data and satisfy them using Bregman projection. Since the projection is not orthogonal, we require an appropriate subset of constraints. In order to select such subset of constraints, we have further developed a framework called MO-SSDARD, which uses an evolutionary algorithm while optimizing various cluster validity indices simultaneously. The experimental results on various datasets show that our proposed method is superior than various methods concerning various validity indices.

关键词： Semi-supervised classification Dimensionality reduction Metric learning Bregman projection evolutionary algorithm

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Using MOEA to Evolve a Combinational Circuit on a FPGA Chip

Using MOEA to Evolve a Combinational Circuit on a FPGA Chip

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7th World Congress on Intelligent Control and Automation

作者： Piao, Changhao Wang, Jin Luo, Zhiyong Chongqing Univ Posts & Telecommun Coll Automat Chongqing 400065 Peoples R China Inha Univ Dept Informat & Commun engn Incheon 402751 South Korea

ISBN: (纸本)9781424421138

A combinational circuit design method that can be completely implemented on a FPGA is presented. For carrying out faster evolution in evolutionary circuit design, a sub population based MOEA (multiobjective evolutionary algorithm) is employed in which the reconfigurable circuit (RC) architecture is encoded by Cartesian Genetic Programming (CGP). For hardware implementation, the Celoxica RC1000 PCI is employed which includes Xilinx Virtex xcv 2000E FPGA chip. This PCI card is communicating with host PC and acting as an evolvable platform. MOEA adopted modules are designed into a FPGA chip for discussing the rationality of circuit design method. Results of direct evolution and results of incremental evolution is compared, it shows MOEA is most efficient in the aspect of speeding up the convergence of evolution.

关键词： Multiobjective evolutionary algorithm Virtual Reconfigurable Circuit FPGA Cartesian Genetic Programming

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A Learning Mechanism for BRBES Using Enhanced Belief Rule-Based Adaptive Differential Evolution 9

A Learning Mechanism for BRBES Using Enhanced Belief Rule-Ba...

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Joint 9th International Conference on Informatics, Electronics and Vision (ICIEV) / 4th International Conference on Imaging, Vision and Pattern Recognition (ICIVPR)

作者： Ul Islam, Raihan Hossain, Mohammad Shahadat Andersson, Karl Lulea Univ Technol Pervas & Mobile Comp Lab Lulea Sweden Univ Chittagong Dept Comp Sci & Engn Chittagong Bangladesh

ISBN: (纸本)9781728193311

Nowadays, belief rule-based expert systems (BRBESs) are widely used in various domains which provides a framework to handle qualitative and quantitative data by addressing several kinds of uncertainty. Learning plays an important role in BRBES to upgrade its knowledge base and parameters values, necessary for the improvement of the prediction accuracy. Different optimal training procedures such as Particle Swarm Optimisation (PSO), Differential Evolution (DE), and Genetic algorithm (GA) have been used as learning mechanisms. Among these procedures, DE performs comparatively better than others. However, DE's performance depends significantly in assigning near optimal values to its control parameters including cross over and mutation factors. Therefore, the objective of this article is to present a novel optimal training procedure by integrating DE with BRBES. This is named as enhanced belief rule-based adaptive differential evolution (eBRBaDE) algorithm because it has the ability to determine the near-optimal values of both the control parameters while ensuring the balanced exploitation and exploration in the search space. In addition, a new joint optimization learning mechanism by using eBRBaDE is presented where both parameter and structure of BRBES are considered. The reliability of the eBRBaDE has been compared with evolutionary optimization algorithms such as GA, PSO, BAT, DE and L-SHADE. This comparison has been carried out by taking account of both conjunctive and disjunctive BRBESs while predicting the Power Usage Effectiveness (PUE) of a datacentre. The comparison demonstrates that the eBRBaDE provides higher prediction accuracy of PUE than from other evolutionary optimization algorithms. Contribution-An enhanced differential evolution algorithm has been proposed in this paper, which is later used as a novel optimal training procedure for BRBES.

关键词： Optimization Learning evolutionary algorithm Differential evolution Belief Rule-based Expert Systems

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An Analysis of the Taguchi Method for Tuning a Memetic algorithm with Reduced Computational Time Budget 31st

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31st International Symposium on Computer and Information Sciences (ISCIS)

作者： Guemues, Dueriye Betuel Oezcan, Ender Atkin, Jason Univ Nottingham Sch Comp Sci ASAP Res Grp Wollaton Rd Nottingham NG8 1BB England

ISBN: (纸本)9783319472171

Determining the best initial parameter values for an algorithm, called parameter tuning, is crucial to obtaining better algorithm performance;however, it is often a time-consuming task and needs to be performed under a restricted computational budget. In this study, the results from our previous work on using the Taguchi method to tune the parameters of a memetic algorithm for cross-domain search are further analysed and extended. Although the Taguchi method reduces the time spent finding a good parameter value combination by running a smaller size of experiments on the training instances from different domains as opposed to evaluating all combinations, the time budget is still larger than desired. This work investigates the degree to which it is possible to predict the same good parameter setting faster by using a reduced time budget. The results in this paper show that it was possible to predict good combinations of parameter settings with a much reduced time budget. The good final parameter values are predicted for three of the parameters, while for the fourth parameter there is no clear best value, so one of three similarly performing values is identified at each time instant.

关键词： evolutionary algorithm Parameter tuning Design of experiments Hyper-heuristic Optimisation

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Optimization of Multi-Mission Space Exploration Campaign Schedules Subject to Stochastic Launch Delay

Optimization of Multi-Mission Space Exploration Campaign Sch...

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AIAA SciTech Forum

作者： Gollins, Nicholas Ho, Koki Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Future plans for deep-space exploration campaigns, targeting locations such as the Moon and Mars, feature a multitude of interdependent missions. For example, a crewed mission may require robotic pre-cursor scouting missions, or supporting cargo delivery missions. As campaign complexity and mission inter-dependency increases, the potential knock-on effects and costs of launch delays also increase. Understanding the potential impacts of delays is an important part of architecture trade-offs in the early definition phase of a project, but quantifying those impacts for a large number of potential architecture solutions is difficult. This work aims to solve this issue by producing a method to both quantify the impact of launch delay and compare possible campaign launch schedules, in order to find robust and near-optimal solutions, by measuring the expected value of the optimization objective and expected probability of infeasibility due to the launch uncertainty. The method is applied specifically to potential lunar exploration architectures in a case study.

关键词： Deep Space Exploration Earth Lunar Exploration Moon Optimization algorithm evolutionary algorithm Space Logistics Optimization Propellant Lunar Orbit NASA

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Development and modeling of a continuous dispersion copolymerization process in the presence of a stabilizing agent elaborated in situ

Development and modeling of a continuous dispersion copolyme...

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9th International Workshop on Polymer Reaction Enginering

作者： Pla, Fernand Fonteix, Christian Hoppe, Sandrine Schrauwen, Cornelius Van der Wal, Hanno Van Damme, Freddy Nancy Univ CNRS Lab Sci Genie Chim F-54001 Nancy France Dow Benelux NV NL-4530 AA Terneuzen Netherlands

This work deals with the development of a process for the radical copolymerization of acrylonitrile and styrene in a dispersed medium. This process was carried out in a continuous stirred tank reactor, in the presence of a stabilizing agent produced in situ during the polymerization. The continuous phase is a polyol. Besides all elementary chemical mechanisms related to the copolymerization and to the synthesis and grafting of the stabilizing agent, this process involves several complex physical phenomena. A tendency model of the whole process was developed, using the corresponding mass balances and thermodynamics. its unknown parameters were identified by use of an evolutionary algorithm and experimental data resulting from an adapted experimental strategy. This model was then validated and allowed to predict monomers and transfer agent conversions, amounts of solids and average molar masses, versus the operating conditions.

关键词： copolymer polyol dispersion polymerization evolutionary algorithm modeling

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Group Contribution Method Supervised Neural Network for Precise Design of Organic Nonlinear Optical Materials

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Precision Chemistry 2024年第6期2卷 263-272页

作者： Jinming Fan Bowei Yuan Chao Qian Shaodong Zhou College of Chemical and Biological Engineering Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture TechnologyZhejiang UniversityHangzhou 310027P.R.China Zhejiang Provincial Innovation Center of Advanced Chemicals Technology Institute of Zhejiang University-QuzhouQuzhou 324000P.R.China

To rationalize the design of D-π-A type organic small-molecule nonlinear optical materials,a theory guided machine learning framework is *** an approach is based on the recognition that the optical property of the molecule is predictable upon accumulating the contribution of each component,which is in line with the concept of group contribution method in *** realize this,a Lewis-mode group contribution method(LGC)has been developed in this work,which is combined with the multistage Bayesian neural network and the evolutionary algorithm to constitute an interactive framework(LGC-msBNN-EA).Thus,different optical properties of molecules are afforded accurately and efficientlyby using only a small data set for ***,by employing the EA model designed specifically for LGC,structural search is well *** origins of the satisfying performance of the framework are discussed in *** that such a framework combines chemical principles and data-driven tools,most likely,it will be proven to be rational and efficient to complete mission regarding structure design in related fields.

关键词： supervised Bayesian neural network Lewis-type group contribution method nonlinear optical material molecule design evolutionary algorithm

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