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.

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.

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.

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.

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.

The benefits and disadvantages of the law on the expansion of the jurisdiction of the DIFC Court

2018 ◽  
Vol 60 (2) ◽  
pp. 221-232
Author(s):  
Tareq Na’el Al-Tawil ◽  
Prabhakar Gantasala ◽  
Hassan Younies
Keyword(s):  
English Language ◽  
Geographical Area ◽  
Legal Framework ◽  
Negative Consequences ◽  
Content Type ◽  
Limited Volume ◽  
Financial Centre ◽  
The World ◽  
Long Time ◽  
The Law

Purpose This paper aims to discuss the benefits and disadvantages of the law on the expansion of the jurisdiction of the Dubai International Financial Centre (DIFC) Court. The major role of DIFC Courts in the Arab community is to handle cases related to commerce and business. For a long time, the court had been acting only in their geographical area until a new law was enacted to extend their jurisdiction all over the world. Afterward, a lot of criticism emerged as for why and how the court will benefit from such actions. The law has drawn a harsh response, although most benefits have also been experienced since the court received quite a large number of new signings. Interaction at the world business forum has benefited the economy of Dubai thanks to the law. Design/methodology/approach The following study focuses on a description of such benefits and drawbacks. The study does not evaluate a factual process of expansion but indicates the most distinct evidence of positive, as well as negative consequences of the expansion. Findings It is appropriate to make a general comment on the fact that the expansion of DIFC Court is not sufficiently effective at the current stage. Needless to say, it contains numerous positive aspects, but the gaps are evidently essential because they place the entire Court in a hard circumstance. The Court does not have a well-developed legal framework for its new area of jurisdiction as long as its limited volume of prior precedent is a distinct sign of the Court’s dependence on the UAE’s Law. In such way, DIFC Court will not be able to address issues within new fields of jurisdiction, as it simply lacks an expertise and international law in its legal framework. Moreover, the jurisdiction over new areas of international business was not verified with a plain system of mediation, which is why a current expansion of DIFC Court has to be recognized as redundant. However, its advantages are tending to produce their effects provided that the Court manages to address its current problems. Originality/value The study has described the basic benefits and drawbacks of DIFC Court expansion. To speak about the main benefits, they can be depicted as appliance of the common law, unification of English language for proceedings, presence of a preliminary arbitration and guarantees of award enforcement. In a similar way, the drawbacks of the expansion have been issued. The study has identified such drawbacks as lack of international and sophisticated expertise, untested legal framework, strong influence of forum non conveniens, and existence of a limited volume of prior precedent. The paper has not assessed a success of a factual expansion of DIFC Court jurisdiction, but it has managed to fulfill its primary purpose. Thus, the paper has identified a certain tendency concerning the expansion.

scholarly journals A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1211
Author(s):  
Ivona Brajević
Keyword(s):  
Integer Programming ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Minimax Problems ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Effective Performance ◽  
Complex Optimization ◽  
Overall Performance ◽  
Integer Programming Problems

The artificial bee colony (ABC) algorithm is a prominent swarm intelligence technique due to its simple structure and effective performance. However, the ABC algorithm has a slow convergence rate when it is used to solve complex optimization problems since its solution search equation is more of an exploration than exploitation operator. This paper presents an improved ABC algorithm for solving integer programming and minimax problems. The proposed approach employs a modified ABC search operator, which exploits the useful information of the current best solution in the onlooker phase with the intention of improving its exploitation tendency. Furthermore, the shuffle mutation operator is applied to the created solutions in both bee phases to help the search achieve a better balance between the global exploration and local exploitation abilities and to provide a valuable convergence speed. The experimental results, obtained by testing on seven integer programming problems and ten minimax problems, show that the overall performance of the proposed approach is superior to the ABC. Additionally, it obtains competitive results compared with other state-of-the-art algorithms.

Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jasleen Kaur ◽  
Punam Rani ◽  
Brahm Prakash Dahiya
Keyword(s):  
Artificial Bee Colony ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Swarm Optimization ◽  
Content Type ◽  
Bee Colony ◽  
Glowworm Swarm Optimization ◽  
Optimal Cluster

Purpose This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency. Design/methodology/approach The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB. Findings The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly Optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay. Originality/value This research work is original.

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