Simultaneous determination of maximum acceleration and endurance of morphing UAV with ABC algorithm-based model

2020 ◽  
Vol 92 (4) ◽  
pp. 579-586 ◽  
Author(s):  
Mehmet Konar
Keyword(s):  
System Design ◽  
Maximum Acceleration ◽  
Content Type ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Equal Importance ◽  
Input Parameters ◽  
Thrust System ◽  
Morphing Uav

Purpose The purpose of this paper is to present a novel approach based on the artificial bee colony (ABC) algorithm aiming to achieve maximum acceleration and maximum endurance for morphing unmanned aerial vehicle (UAV) design. Design/methodology/approach Some of the most important issues in the design of UAV are the design of thrust system and determination of the endurance of the UAV. Although propeller selection is very important for the thrust system design, battery selection has the utmost importance for the determination of UAV endurance. In this study, the calculations of maximum acceleration and endurance required by ZANKA-II during the flight are considered simultaneously. For this purpose, a model based on the ABC algorithm is proposed for the morphing UAV design, aiming to achieve the maximum acceleration and endurance. In the proposed model, the propeller diameter, propeller pitch and battery values used in morphing UAV's power system design are selected as the input parameters; maximum acceleration and endurance are selected as the output parameters. To obtain the maximum acceleration and endurance, the optimum input parameters are determined through the ABC algorithm-based model. Findings Considerable improvements on maximum acceleration and endurance of morphing UAV with ABC algorithm-based model are obtained. Research limitations/implications The endurance and acceleration due to the thrust are two separate parameters that are not normally proportional to each other. In this study, optimization of UAV’s endurance and acceleration is considered with equal importance. Practical implications Using artificial intelligence techniques causes fast and simple optimization for determination of UAV’s endurance and acceleration with equal importance. In the simulation studies with ABC algorithm, satisfactory results are obtained. Social implications The results of the study have showed that the proposed approach could be an alternative method for UAV designers. Originality/value Providing a new and efficient method saves time and reduces cost in calculations of maximum acceleration and endurance of the UAV.

Improvement of the thrust-torque ratio of an unmanned helicopter by using the ABC algorithm

2020 ◽  
Vol 92 (8) ◽  
pp. 1133-1139
Author(s):  
Mehmet Konar ◽  
Aydin Turkmen ◽  
Tugrul Oktay
Keyword(s):  
Mass Density ◽  
Twist Angle ◽  
Unmanned Helicopter ◽  
Content Type ◽  
Abc Algorithm ◽  
Single Output ◽  
Bee Colony ◽  
Long Time ◽  
Rotating Wing ◽  
Maximum Thrust

Purpose The purpose of this paper is to use an ABC algorithm to improve the thrust–torque ratio of a rotating-wing unmanned aerial vehicle (UAV) model. Design/methodology/approach The design of UAVs, such as aircraft, drones, helicopters, has become one of the popular engineering areas with the development of technology. This study aims to improve the value of thrust–torque ratio of an unmanned helicopter. For this purpose, an unmanned helicopter was built at the Faculty of Aeronautics and Astronautics, Erciyes University. The maximum thrust–torque ratio was calculated considering the blade length, blade chord width, blade mass density and blade twist angle. For calculation, artificial bee colony (ABC) algorithm was used. By using ABC algorithm, the maximum thrust–torque ratio was obtained against the optimum input values. For this purpose, a model with four inputs and a single output is formed. In the generated system model, optimum thrust–torque ratio was calculated by changing the input values used in the ±5% range. As a result of this study, approximately 31% improvement was achieved. According to these results, the proposed approach will provide convenience to the designers in the design of the rotating-wing UAV. Findings According to these results, approximately 31% improvement was achieved, and the proposed approach will provide convenience to the designers in the design of the rotating-wing UAV. Research limitations/implications It takes a long time to obtain the optimum thrust–torque ratio value through the ABC algorithm method. Practical implications Using ABC algorithm provides to improve the value of thrust–torque ratio of an unmanned helicopter. With this algorithm, unmanned helicopter flies more than ever. Thus, the presented method based on the ABC algorithm is more efficient. Social implications The application of the ABC algorithm method can be used effectively to calculate the thrust–torque ratio in UAV. Originality/value Providing an original and penetrating a method that saves time and reduces the cost to improve the value of thrust–torque ratio of an unmanned helicopter.

Neural network based redesign of morphing UAV for simultaneous improvement of roll stability and maximum lift/drag ratio

2018 ◽  
Vol 90 (8) ◽  
pp. 1203-1212 ◽  
Author(s):  
Tugrul Oktay ◽  
Seda Arik ◽  
Ilke Turkmen ◽  
Metin Uzun ◽  
Harun Celik
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Objective Function ◽  
Aircraft Design ◽  
Stability Coefficient ◽  
Content Type ◽  
Abc Algorithm ◽  
Artificial Intelligence Methods ◽  
Drag Ratio ◽  
Morphing Uav

Purpose The aim of this paper is to redesign of morphing unmanned aerial vehicle (UAV) using neural network for simultaneous improvement of roll stability coefficient and maximum lift/drag ratio. Design/methodology/approach Redesign of a morphing our UAV manufactured in Faculty of Aeronautics and Astronautics, Erciyes University is performed with using artificial intelligence techniques. For this purpose, an objective function based on artificial neural network (ANN) is obtained to get optimum values of roll stability coefficient (Clβ) and maximum lift/drag ratio (Emax). The aim here is to save time and obtain satisfactory errors in the optimization process in which the ANN trained with the selected data is used as the objective function. First, dihedral angle (φ) and taper ratio (λ) are selected as input parameters, C*lβ and Emax are selected as output parameters for ANN. Then, ANN is trained with selected input and output data sets. Training of the ANN is possible by adjusting ANN weights. Here, ANN weights are adjusted with artificial bee colony (ABC) algorithm. After adjusting process, the objective function based on ANN is optimized with ABC algorithm to get better Clβ and Emax, i.e. the ABC algorithm is used for two different purposes. Findings By using artificial intelligence methods for redesigning of morphing UAV, the objective function consisting of C*lβ and Emax is maximized. Research limitations/implications It takes quite a long time for Emax data to be obtained realistically by using the computational fluid dynamics approach. Practical implications Neural network incorporation with the optimization method idea is beneficial for improving Clβ and Emax. By using this approach, low cost, time saving and practicality in applications are achieved. Social implications This method based on artificial intelligence methods can be useful for better aircraft design and production. Originality/value It is creating a novel method in order to redesign of morphing UAV and improving UAV performance.

Fatigue crack growth prediction of 7075 aluminum alloy based on the GMSVR model optimized by the artificial bee colony algorithm

2017 ◽  
Vol 34 (4) ◽  
pp. 1034-1053 ◽  
Author(s):  
Dalian Yang ◽  
Yilun Liu ◽  
Songbai Li ◽  
Jie Tao ◽  
Chi Liu ◽  
...  
Keyword(s):  
Aluminum Alloy ◽  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Prediction Accuracy ◽  
Artificial Bee Colony ◽  
7075 Aluminum Alloy ◽  
Prediction Ability ◽  
Content Type ◽  
Abc Algorithm ◽  
Bee Colony

Purpose The aim of this paper is to solve the problem of low accuracy of traditional fatigue crack growth (FCG) prediction methods. Design/methodology/approach The GMSVR model was proposed by combining the grey modeling (GM) and the support vector regression (SVR). Meanwhile, the GMSVR model parameter optimal selection method based on the artificial bee colony (ABC) algorithm was presented. The FCG prediction of 7075 aluminum alloy under different conditions were taken as the study objects, and the performance of the genetic algorithm, the particle swarm optimization algorithm, the n-fold cross validation and the ABC algorithm were compared and analyzed. Findings The results show that the speed of the ABC algorithm is the fastest and the accuracy of the ABC algorithm is the highest too. The prediction performances of the GM (1, 1) model, the SVR model and the GMSVR model were compared, the results show that the GMSVR model has the best prediction ability, it can improve the FCG prediction accuracy of 7075 aluminum alloy greatly. Originality/value A new prediction model is proposed for FCG combined the non-equidistant grey model and the SVR model. Aiming at the problem of the model parameters are difficult to select, the GMSVR model parameter optimization method based on the ABC algorithm was presented. the results show that the GMSVR model has better prediction ability, which increase the FCG prediction accuracy of 7075 aluminum alloy greatly.

Identification of Duhem-type piezoelectric hysteresis using a modified bee colony algorithm

2019 ◽  
Vol 39 (2) ◽  
pp. 249-264
Author(s):  
Qun Chen ◽  
Zong-Xiao Yang
Keyword(s):  
Parameter Identification ◽  
Optimal Solution ◽  
Identification Accuracy ◽  
Content Type ◽  
Bee Colony ◽  
Hysteresis Nonlinearity ◽  
Series Of Experiments ◽  
Searching Ability ◽  
Determination Of Parameters

Purpose The determination of parameters of Duhem model that can describe piezoelectric hysteresis is usually a great challenge. The purpose of this paper is to find a way to identify the parameters of Duhem model by using a modified bee colony algorithm. Design/methodology/approach The promising bee colony algorithm has great potential to identify hysteresis nonlinearity, but has not yet been used to identify the Duhem-type hysteresis in the literatures. To explore this possibility, the classical bee colony algorithm is modified to enhance its performance regarding both searching capability and convergence speed. In the modification, the current optimal solution is used to guide the search direction, which can balance the local and global searching ability. Moreover, a new searching formula for scout bees is proposed to enhance the convergence ability of the algorithm. Findings Through a series of experiments, the modified algorithm can attain the optimal parameters with a 0.61 µm peak valley error and a 0.12 µm root-mean-square error. Compared to the particle swarm optimization and classical bee colony algorithms, the modified bee colony algorithm can reach higher parameter identification accuracy. Based on 50 trials, the robustness of the posed algorithm was also proved. Originality/value The well-performed modified bee colony algorithm is a good candidate in parameter identification of Duhem-type hysteresis nonlinear systems. As there is no work studying the parameter identification of Duhem model using a bee colony algorithm in the literatures, this work closed this gap and explored the ability of bee colony algorithm to identify piezoelectric hysteresis with superb accuracy and robustness.

scholarly journals Islanded microgrid congestion control by load prioritization and shedding using ABC algorithm

2020 ◽  
Vol 10 (5) ◽  
pp. 4552
Author(s):  
L. O. Mogaka ◽  
G. N. Nyakoe ◽  
Michael J. Saulo
Keyword(s):  
Power Systems ◽  
Distribution Systems ◽  
Renewable Energy Sources ◽  
Congestion Management ◽  
Gradual Change ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Load Demand ◽  
Transmission And Distribution

The continued growth in load demand and the gradual change of generation sources to smaller distributed plants utilizing renewable energy sources (RESs), which supply power intermittently, is likely to strain existing power systems and cause congestion. Congestion management still remains a challenging issue in open access transmission and distribution systems. Conventionally, this is achieved by load shedding and generator rescheduling. In this study, the control of the system congestion on an islanded micro grid (MG) supplied by RESs is analyzed using artificial bee colony (ABC) algorithm. Different buses are assigned priority indices which forms the basis of the determination of which loads and what amount of load to shed at any particular time during islanding mode operation. This is to ensure as minimal load as possible is shed during a contingency that leads to loss of mains and ensure a congestion free microgrid operation. This is tested and verified on a modified IEEE 30-bus distribution systems on MATLAB platform. The results are compared with other algorithms to prove the applicability of this approach.

A new fuzzy random multi-objective portfolio model with different entropy measures using fuzzy programming based on artificial bee colony algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xue Deng ◽  
Xiaolei He ◽  
Cuirong Huang
Keyword(s):  
Artificial Bee Colony ◽  
Fuzzy Programming ◽  
Optimal Portfolio ◽  
Content Type ◽  
Abc Algorithm ◽  
Multi Objective ◽  
Bee Colony ◽  
Proposed Model ◽  
Entropy Measures ◽  
Fuzzy Random

PurposeThis paper proposes a fuzzy random multi-objective portfolio model with different entropy measures and designs a hybrid algorithm to solve the proposed model.Design/methodology/approachBecause random uncertainty and fuzzy uncertainty are often combined in a real-world setting, the security returns are considered as fuzzy random numbers. In the model, the authors also consider the effects of different entropy measures, including Yager's entropy, Shannon's entropy and min-max entropy. During the process of solving the model, the authors use a ranking method to convert the expected return into a crisp number. To find the optimal solution efficiently, a fuzzy programming technique based on artificial bee colony (ABC) algorithm is also proposed.Findings(1) The return of optimal portfolio increases while the level of investor risk aversion increases. (2) The difference of the investment weights of the optimal portfolio obtained with Yager's entropy are much smaller than that of the min–max entropy. (3) The performance of the ABC algorithm on solving the proposed model is superior than other intelligent algorithms such as the genetic algorithm, differential evolution and particle swarm optimization.Originality/valueTo the best of the authors' knowledge, no effect has been made to consider a fuzzy random portfolio model with different entropy measures. Thus, the novelty of the research is constructing a fuzzy random multi-objective portfolio model with different entropy measures and designing a hybrid fuzzy programming-ABC algorithm to solve the proposed model.

Analysis of methodologies for determination of DEM input parameters

2021 ◽  
Author(s):  
Rondinelli M. Lima ◽  
Rodolfo J. Brandao ◽  
Raphael L. Santos ◽  
Claudio R. Duarte ◽  
Marcos A. S. Barrozo
Keyword(s):  
Input Parameters

A quasi-affine transformation artificial bee colony algorithm for global optimization

2021 ◽  
pp. 1-18
Author(s):  
Baohua Zhao ◽  
Tien-Wen Sung ◽  
Xin Zhang
Keyword(s):  
Affine Transformation ◽  
Artificial Bee Colony ◽  
Triangular Matrix ◽  
Convergence Speed ◽  
Test Set ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Search Mechanism ◽  
Multiple Dimensions ◽  
Collaborative Search

The artificial bee colony (ABC) algorithm is one of the classical bioinspired swarm-based intelligence algorithms that has strong search ability, because of its special search mechanism, but its development ability is slightly insufficient and its convergence speed is slow. In view of its weak development ability and slow convergence speed, this paper proposes the QABC algorithm in which a new search equation is based on the idea of quasi-affine transformation, which greatly improves the cooperative ability between particles and enhances its exploitability. During the process of location updating, the convergence speed is accelerated by updating multiple dimensions instead of one dimension. Finally, in the overall search framework, a collaborative search matrix is introduced to update the position of particles. The collaborative search matrix is transformed from the lower triangular matrix, which not only ensures the randomness of the search, but also ensures its balance and integrity. To evaluate the performance of the QABC algorithm, CEC2013 test set and CEC2014 test set are used in the experiment. After comparing with the conventional ABC algorithm and some famous ABC variants, QABC algorithm is proved to be superior in efficiency, development ability, and robustness.

scholarly journals The Simulation Design of Microwave Absorption Performance for the Multi-Layered Carbon-Based Nanocomposites Using Intelligent Optimization Algorithm

Nanomaterials ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1951
Author(s):  
Danfeng Zhang ◽  
Congai Han ◽  
Haiyan Zhang ◽  
Bi Zeng ◽  
Yun Zheng ◽  
...  
Keyword(s):  
Layer Structure ◽  
Optimal Solution ◽  
Single Layer ◽  
Simulation Design ◽  
Wide Bandwidth ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Absorbing Material ◽  
Important Means ◽  
Carbon Based

The optimal design objectives of the microwave absorbing (MA) materials are high absorption, wide bandwidth, light weight and thin thickness. However, it is difficult for single-layer MA materials to meet all of these requirements. Constructing multi-layer structure absorbing coating is an important means to improve performance of MA materials. The carbon-based nanocomposites are excellent MA materials. In this paper, genetic algorithm (GA) and artificial bee colony algorithm (ABC) are used to optimize the design of multi-layer materials. We selected ten kinds of materials to construct the multi-layer absorbing material and optimize the performance. Two algorithms were applied to optimize the two-layer MA material with a total thickness of 3 mm, and it was found that the optimal bandwidth was 8.12 GHz and reflectivity was −53.4 dB. When three layers of MA material with the same thickness are optimized, the ultra-wide bandwidth was 10.6 GHz and ultra-high reflectivity was −84.86 dB. The bandwidth and reflectivity of the optimized material are better than the single-layer material without optimization. Comparing the GA and the ABC algorithm, the ABC algorithm can obtain the optimal solution in the shortest time and highest efficiency. At present, no such results have been reported.

News personalization for peace: how algorithmic recommendations can impact conflict coverage

2019 ◽  
Vol 30 (3) ◽  
pp. 309-328 ◽  
Author(s):  
Mariella Bastian ◽  
Mykola Makhortykh ◽  
Tom Dobber
Keyword(s):  
Conceptual Framework ◽  
System Design ◽  
News Media ◽  
Content Distribution ◽  
Content Type ◽  
Constructive Conflict ◽  
Peace Journalism ◽  
Negative Impacts ◽  
The Impact ◽  
War Reporting

PurposeThe purpose of this paper is to develop a conceptual framework for assessing what are the possibilities and pitfalls of using algorithmic systems of news personalization – i.e. the tailoring of individualized news feeds based on users’ information preferences – for constructive conflict coverage in the context of peace journalism, a journalistic paradigm calling for more diversified and creative war reporting.Design/methodology/approachThe paper provides a critical review of existing research on peace journalism and algorithmic news personalization, and analyzes the intersections between the two concepts. Specifically, it identifies recurring pitfalls of peace journalism based on empirical research on constructive conflict coverage and then introduces a conceptual framework for analyzing to what degree these pitfalls can be mediated – or worsened – through algorithmic system design.FindingsThe findings suggest that AI-driven distribution technologies can facilitate constructive war reporting, in particular by countering the effects of journalists’ self-censorship and by diversifying conflict coverage. The implementation of these goals, however, depends on multiple system design solutions, thus resonating with current calls for more responsible and value-sensitive algorithmic design in the domain of news media. Additionally, our observations emphasize the importance of developing new algorithmic literacies among journalists both to realize the positive potential of AI for promoting peace and to increase the awareness of possible negative impacts of new systems of content distribution.Originality/valueThe article particle is the first to provide a comprehensive conceptualization of the impact of new content distribution techniques on constructive conflict coverage in the context of peace journalism. It also offers a novel conceptual framing for assessing the impact of algorithmic news personalization on reporting traumatic and polarizing events, such as wars and violence.

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