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A survey on dragonfly algorithm and its applications in engineering

EVOLUTIONARY INTELLIGENCE 2023年第1期16卷 1-21页

作者： Rahman, Chnoor M. Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Fattah, Polla Mirjalili, Seyedali Charmo Univ Coll Hlth & Appl Sci Appl Comp Dept Sulaimany Krg Iraq Univ Kurdistan Hewler Sch Sci & Engn Comp Sci & Engn Dept Erbil Krg Iraq Charles Sturt Univ Sch Comp & Math Sydney NSW Australia Western Sydney Univ Sch Comp Engn & Math Sydney City Campus Sydney NSW Australia Asia Pacific Int Coll APIC Informat Technol Dept Sydney NSW Australia Kent Inst Australia Informat Technol Dept Sydney NSW Australia Singidunum Univ Belgrade Serbia Salahaddin Univ Erbil Software & Informat Engn Erbil Krg Iraq Torrens Univ Ctr Artificial Intelligence Res & Optimisat Adelaide SA Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

The dragonfly algorithm was developed in 2016. It is one of the algorithms used by researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examined the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. The utilized engineering applications are the applications in the field of mechanical engineering problems, electrical engineering problems, optimal parameters, economic load dispatch, and loss reduction. The algorithm is tested and evaluated against particle swarm optimization algorithm and firefly algorithm. To evaluate the ability of the dragonfly algorithm and other participated algorithms a set of traditional benchmarks (TF1-TF23) were utilized. Moreover, to examine the ability of the algorithm to optimize large-scale optimization problems CEC-C2019 benchmarks were utilized. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with participated algorithms (GWO, PSO, and GA), the dragonfly algorithm owns an excellent performance,

关键词： Dragonfly algorithm Swarm intelligence Metaheuristic algorithm optimization algorithm Single and multi-objective optimization DA

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Efficiency analysis of dual motor powertrain with planetary gear set

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2023年第5期24卷 601-608页

作者： Mishra, Shivam Dominic, Arun Mudliyar, Karuna Divakar, Manjula Natl Inst Technol Surathkal Dept Elect & Elect Engn Surathkal Karnataka India Daimler Truck Innovat Ctr Bengaluru India

This paper focuses on electric vehicles with dual motors combined with a planetary gear train. The combination of sun and ring motor allows for achieving higher efficiencies and satisfying power demands as compared to single motor powertrains. Primarily dual motor drive with planetary gear train is proposed and simulated with different drive cycles. Secondarily by extracting the motor efficiency maps working area of different modes, speed and efficiency are determined. Finally, an optimization algorithm was developed to automatically change speed and torque as per load demand. In this way, our work could represent a framework for improving efficiency performances and load demands of an electric vehicle.

关键词： dual motor drive e-CVT motor efficiency optimization algorithm planetary gear train

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Sequential Convex Programming Using Augmented Lagrange Multiplier Method and Concave-Convex Decomposition

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2023年第4期46卷 718-725页

作者： Jun, Li Sifeng, Zhang Zhenyu, Jiang Xibin, Bai Huabo, Yang Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China

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optimization of Artificial Bee Colony algorithm Based on Immune Regulation 3

Optimization of Artificial Bee Colony Algorithm Based on Imm...

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3rd International Conference on Smart BlockChain (IEEE SmartBlock)

作者： Zeng, Xiangshi Zhang, Congpin Lei, Tiantian Wei, Yifan Henan Normal Univ Xinxiang 453007 Henan Peoples R China Henan Engn Lab Teaching Resources & Big Data Educ Evaluat Xinxiang Henan Peoples R China

ISBN: (纸本)9781665440738

In this paper, aiming at the shortcomings of the Artificial Bee Colony (ABC) algorithm in guiding bees and observing bees in the search mechanism, the author integrated the idea of self-regulation of antibody concentration in the immune algorithm, regarded the optimal solution as antigen, and the search bee as antibody for target search. In this paper, the differences between the optimal solution and the initial solution are compared by setting parameters when initializing the nectar source, so as to enhance the memory of antibodies, avoid falling into the local optimal when maintaining the diversity of the population, accelerate the convergence speed, and increase the global search ability of the algorithm. Simulation results of six classical test functions show that the proposed algorithm has obvious advantages over ABC algorithm in terms of optimization accuracy, convergence, accuracy and stability. We will combine the proposed algorithm with Blockchain techniques in our future work.

关键词： immune regulation artificial bee colony optimization algorithm blockchain

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A Novel Software Tool for Automated and Integrated Building Energy Model Calibration 11th

A Novel Software Tool for Automated and Integrated Building ...

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11th International Conference of Ar.Tec.

作者： Maracchini, Gianluca D'Orazio, Marco Di Giuseppe, Elisa Revel, Gian Marco Univ Trento Dept Civil Environm & Mech Engn I-38123 Trento Italy Marche Polytech Univ Dept Construct Civil Engn & Architecture I-60131 Ancona Italy Marche Polytech Univ Dept Ind Engn & Math Sci I-60131 Ancona Italy

ISBN: (纸本)9783031718656;9783031718632;9783031718625

In the EU, the building sector significantly impacts energy consumption and greenhouse gas emissions, accounting for 40% of the total energy use and 35% of emissions, mainly due to the energy inefficiency of the building stock. With energy demand expected to increase over the next decade, improving building energy efficiency is essential for meeting EU sustainability goals. Building Energy Models (BEMs) are crucial for evaluating and enhancing building performance throughout their lifecycle. However, a notable "energy performance gap" usually exists between predicted and actual energy use, exacerbated by challenges in accurately inputting numerous variables and the simplifications inherent in modeling. BEM calibration (BC) approaches are often adopted to reduce these discrepancies, aimed at adjusting model inputs to match output with the observed data. Yet, there is not a universal consensus on which is the best calibration method, with manual and automated approaches offering different benefits. Automated methods, especially those using optimization algorithms, have gained prominence for their efficiency and ability to handle uncertainties. However, BC still significantly depends on the energy modelers' expertise. This paper introduces a novel software tool for automated BC, aiming to simplify the process by integrating expert knowledge, sensitivity analysis, and optimization algorithms techniques in a unique workflow. This tool reduces the dependence of BC success on modeler expertise, representing a significant step towards more accessible automated BC in the research field and engineering practice, thence allowing a more effective design of energy conservation measures.

关键词： Building Energy Model Model calibration optimization algorithm Sensitivity analysis Software

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Arch bridge damage detection using vibration data and gradient-based optimizer algorithm

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2025年第2期10卷 1-12页

作者： Mohebian, Pooya Motahari, Mohammad Reza Aval, Seyed Bahram Beheshti Arak Univ Fac Engn Dept Civil Engn Arak Iran KN Toosi Univ Technol Fac Civil Engn Tehran Iran

Bridges are one of the most pivotal civil engineering structures. Nevertheless, they are highly susceptible to localized damage, which poses a gradual threat to their long-term stability and functionality. Hence, it is of utmost importance to detect any damage in bridges to ensure their continued safe and reliable performance. In view of this, the present study proposes an optimization-based finite element model updating method to perform arch bridge damage detection by using vibration data and the gradient-based optimizer (GBO) algorithm. The damage severity of structural elements in different parts of the bridge, including those in the arch, columns, and deck, are taken as damage variables. The objective function of the optimization problem is also defined in terms of discrepancies between the vibration data of the actual damaged bridge and those computed from the finite element model. The GBO algorithm, as a new robust meta-heuristic algorithm, is applied to tackle the damage detection problem, and its performance is compared to other well-known meta-heuristic algorithms. The applicability of the proposed method is numerically assessed on an arch bridge structure under both noise-free and noisy conditions. The obtained results demonstrate the accuracy and computational efficiency of the presented method in dealing with the arch bridge damage detection problem.

关键词： Damage detection Arch bridge Vibration method Finite element model updating optimization algorithm

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Usage of ChatGPT for Engineering Design and Analysis Tool Development

Usage of ChatGPT for Engineering Design and Analysis Tool De...

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AIAA SciTech Forum

作者： Pierson, Kristopher C. Ha, Matthew J. NASA Glenn Res Ctr Multiscale & Multiphys Modeling Branch Brookpark OH 44135 USA NASA Glenn Res Ctr HX5 LLC Turbomachinery & Turboelect Syst Branch Brookpark OH 44135 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

ChatGPT, a generative AI large language model, has recently captured significant attention in both the computer science community and the broader public domain. It has demonstrated a wide range of capabilities, from answering simple questions to writing fully functional computer code. This study spotlights both the capabilities and limitations of ChatGPT when addressing engineering problems. The model's capacity to generate practical engineering tools is highlighted through an example of a prompt that leads to an interactive plotting tool, enabling the examination of the fluid boundary layer around a fan blade. Subsequently, the paper also uncovers potential pitfalls in ChatGPT's application, shown through an unsuccessful attempt to use ChatGPT to automate a process in Ansys Workbench through scripting. The research further investigates ChatGPT's proficiency in addressing inquiries and providing explanations about the functionalities of OpenMDAO, an opensource, multidisciplinary design, analysis, and optimization tool developed at NASA Glenn Research Center. Finally, an optimization methodology, developed with ChatGPT's help, is applied to the structural optimization of a fan blade. The developed optimization method utilizes T-Blade3 for geometry generation, Ansys Mechanical for meshing and finite element analysis, and sci-kit learn's MLPRegressor method to generate a trained neural network model of the design space. OpenMDAO is then used to find the optimal point within the design space. The outcome is a significant reduction in stress in the optimized model-less than onefifth of the stress value in the baseline model.

关键词： ANSYS Neural Networks Boundary Layers OpenMDAO Turbomachinery optimization algorithm Finite Element Analysis NASA glenn research center Structural optimization Python

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EFFECT OF LAYER TIME CONTROL VIA PHYSICS-BASED MODEL ON LAYER TEMPERATURE IN EXTRUSION-BASED LARGE FORMAT ADDITIVE MANUFACTURING 19

EFFECT OF LAYER TIME CONTROL VIA PHYSICS-BASED MODEL ON LAYE...

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ASME 19th International Manufacturing Science and Engineering Conference (MSEC) held jointly with the 52nd North American Research Conference (NAMRC)

作者： Jo, Eonyeon Liu, Lu Ju, Feng Garg, Nikhil Ozcan, Soydan Vaidya, Uday Kim, Seokpum Univ Tennessee Bredesen Ctr Knoxville TN USA Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ USA Oak Ridge Natl Lab Mfg Sci Div Oak Ridge TN 37830 USA Univ Tennessee Dept Mech Aerosp & Biomed Engn Knoxville TN USA

ISBN: (纸本)9780791888100

In polymer extrusion-based large format additive manufacturing (AM), the strength and structural integrity of printed parts depend on interlayer bonding, which is highly affected by layer temperature. Layer deposition with an improper layer temperature can result in undesirable defects such as warping, cracking, and debonding. A critical factor in this layer deposition process is the layer deposition time, commonly known as "layer time," which significantly impacts layer temperature and interlayer bonding strength. For example, an excessively long layer time results in an over-cooled surface, leading to interlayer debonding, cracking, and warping. Conversely, an excessively short layer time causes overheating, leading to material collapse due to insufficient stiffness when new layers are deposited. Traditionally, layer time have been determined based on the experience of skilled operators, employing a fixed rate throughout the entire printing process. This approach often fails to address dynamic changes during the printing process. Furthermore, it is limited by the absence of comprehensive guidelines based on accurate data and systematic analysis. Determining the best optimal layer time is further complicated by the influence of variable environmental conditions, geometric complexities, and printing parameters. There exists a clear need for a layer time control framework that can determine the best optimal layer time to achieve the desired layer temperature across all deposited layers, ultimately leading to an optimal printing outcome. In response to these challenges, a finite element analysis (FEA) model was used to simulate the additive deposition process. After the deposition of each layer, the simulation process paused, and the temperature data was fed into the previously developed layer time optimization framework. With the optimization framework, an optimal layer time for each successive layer was determined based on the cooling behavior of previously deposite

关键词： Large format additive manufacturing layer deposition time layer time control finite element analysis optimization algorithm

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Response Limiting in Shaker Shocks

Response Limiting in Shaker Shocks

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AIAA SciTech Forum

作者： Babuska, Vit Cap, Jerome S. Sandia Natl Labs Environments Engn Dept 1557 Albuquerque NM 87185 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

The primary goal of any laboratory test is to expose the unit-under-test to conservative realistic representations of a field environment. Satisfying this objective is not always straightforward due to laboratory equipment constraints. For vibration and shock tests performed on shakers over-testing and unrealistic failures can result because the control is a base acceleration and mechanical shakers have nearly infinite impedance. Force limiting and response limiting are relatively standard practices to reduce over-test risks in random-vibration testing. Shaker controller software generally has response limiting as a built-in capability and it is done without much user intervention since vibration control is a closed loop process. Limiting in shaker shocks is done for the same reasons, but because the duration of a shock is only a few milliseconds, limiting is a pre-planned user in the loop process. Shaker shock response limiting has been used for at least 30 years at Sandia National Laboratories, but it seems to be little known or used in industry. This objective of this paper is to re-introduce response limiting for shaker shocks to the aerospace community. The process is demonstrated on the BARBECUE testbed.

关键词： Shock Response Spectrum Closed Loop Control Vibration Control Aerospace optimization algorithm Accelerometer 3D Printing Stress Concentration Satellites NASA

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Optimal orbit populating strategy of a versatile micro-launcher upper stage for multi-small satellites missions

Optimal orbit populating strategy of a versatile micro-launc...

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AIAA SciTech Forum

作者： Bourgeois, Eric Dupuis, Arnaud CNES Launcher Directorate Performance & Control Dept 52 Rue Jacques Hillairet F-75012 Paris France

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Many New Space initiatives run on new micro-launchers delivering a set of satellites on their specific targeted orbits. It opens the question of designing a sequence ordering the orbit visits, including initial and final orbits constrained respectively by launcher performance and end-of-life requirements. An optimization problem is defined, consisting in minimizing the propellant consumption (which is restricted by the upper stage propellant loading), under orbit constraints. The parameters are the orbit visit order, and associated maneuver characteristics. A specific method handling this Mixed Integer Non Linear Programming problem, its implementation and validation are described, including a micro-launcher representative study case.

关键词： Small Satellites Cryogenic Propellants optimization algorithm Orbit Determination Perigee Orbital Perturbations Flight Dynamics Branch and Bound algorithm Thrust to Weight Ratio Rocket Engine

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