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Article
Publication date: 25 August 2020

Aziz Kaba and Emre Kiyak

The purpose of this paper is to introduce an artificial bee colony-based Kalman filter algorithm along with an extended objective function to ensure the optimality of the…

Abstract

Purpose

The purpose of this paper is to introduce an artificial bee colony-based Kalman filter algorithm along with an extended objective function to ensure the optimality of the estimator of the quadrotor in the presence of unknown measurement noise statistics.

Design/methodology/approach

Six degree-of-freedom mathematical model of the quadrotor is derived. Position controller for the quadrotor is designed. Kalman filter-based estimation algorithm is implemented in the sensor feedback loop. Artificial bee colony-based hybrid algorithm is used as an optimization method to handle the unknown noise statistics. Existing objective function is extended with a penalty term. Mathematical proof of the extended objective function is derived. Results of the proposed algorithm is compared with de facto genetic algorithm-based Kalman filter.

Findings

Artificial bee colony algorithm-based Kalman filter and extended objective function duo are able to optimize the measurement noise covariance matrix with an absolute error as low as 0.001 [m2]. Proposed method and function is capable of reducing the noise from 2 to 0.09 [m] for x-axis, 3.4 to 0.14 [m] for y-axis and 3.7 to 0.2 [m] for z-axis, respectively.

Originality/value

The motivation behind this paper is to bring a novel optimization-based solution for the estimation problem of the quadrotor when the measurement noise statistics are unknown along with an extended objective function to prevent the infeasible solutions with mathematical convergence analysis.

Details

Aircraft Engineering and Aerospace Technology, vol. 92 no. 10
Type: Research Article
ISSN: 1748-8842

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Article
Publication date: 29 November 2019

Qun Chen and Zong-Xiao Yang

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…

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Abstract

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.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 39 no. 2
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 15 March 2013

Sun Changhao and Haibin Duan

The purpose of this paper is to propose a new algorithm for pendulum‐like oscillation control of an unmanned rotorcraft (UR) in a reconnaissance mission and improve the…

Abstract

Purpose

The purpose of this paper is to propose a new algorithm for pendulum‐like oscillation control of an unmanned rotorcraft (UR) in a reconnaissance mission and improve the stabilizing performance of the UR's hover and stare.

Design/methodology/approach

The algorithm is based on linear‐quadratic regulator (LQR), of which the determinable parameters are optimized by the artificial bee colony (ABC) algorithm, a newly developed algorithm inspired by swarm intelligence and motivated by the intelligent behaviour of honey bees.

Findings

The proposed algorithm is tested in a UR simulation environment and achieves stabilization of the pendulum oscillation in less than 4s.

Research limitations/implications

The presented algorithm and design strategy can be extended for other types of complex control missions where relative parameters must be optimized to get a better control performance.

Practical implications

The ABC optimized control system developed can be easily applied to practice and can safely stabilize the UR during hover and stare, which will considerably improve the stability of the UR and lead to better reconnaissance performance.

Originality/value

This research presents a new algorithm to control the pendulum‐like oscillation of URs, whose performance of hover and stare is a key issue when carrying out new challenging reconnaissance missions in urban warfare. Simulation results show that the presented algorithm performs better than traditional methods and the design process is simpler and easier.

Details

Aircraft Engineering and Aerospace Technology, vol. 85 no. 2
Type: Research Article
ISSN: 0002-2667

Keywords

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Article
Publication date: 25 February 2014

Yimin Deng and Haibin Duan

The purpose of this paper is to propose a biological edge detection approach for aircraft such as unmanned combat air vehicle (UCAV), with the objective of making the UCAV…

Abstract

Purpose

The purpose of this paper is to propose a biological edge detection approach for aircraft such as unmanned combat air vehicle (UCAV), with the objective of making the UCAV recognize targets, especially in complex noisy environment.

Design/methodology/approach

The hybrid model of saliency-based visual attention and artificial bee colony (ABC) algorithm is established for edge detection of UCAV. Visual attention can extract the region of interesting objects, and this approach can narrow the searching region for object segmentation, which can reduce the computational complexity. An improved ABC algorithm is applied in edge detection of the salient region.

Findings

This work improved ABC algorithm by modifying the search strategy and adding some limits, so that it can be applied to edge detection problem. A hybrid model of saliency-based visual attention and ABC algorithm is developed. Experimental results demonstrated the feasibility and effectiveness of the proposed method: it can guarantee efficient target localization, with accurate edge detection in complex noisy environment.

Practical implications

The biological edge detection model developed in this paper can be easily applied to practice and can steer the UCAV during target recognition, which will considerably increase the autonomy of the UCAV.

Originality/value

A hybrid model of saliency-based visual attention and ABC algorithm is proposed for biological edge detection. An improved ABC algorithm is applied in edge detection of the salient region.

Details

Aircraft Engineering and Aerospace Technology: An International Journal, vol. 86 no. 2
Type: Research Article
ISSN: 0002-2667

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Article
Publication date: 3 June 2014

Qinan Luo and Haibin Duan

Artificial bee colony (ABC) algorithm is a relatively new optimization method inspired by the herd behavior of honey bees, which shows quite intelligence. The purpose of…

Abstract

Purpose

Artificial bee colony (ABC) algorithm is a relatively new optimization method inspired by the herd behavior of honey bees, which shows quite intelligence. The purpose of this paper is to propose an improved ABC optimization algorithm based on chaos theory for solving the push recovery problem of a quadruped robot, which can tune the controller parameters based on its search mechanism. ADAMS simulation environment is adopted to implement the proposed scheme for the quadruped robot.

Design/methodology/approach

Maintaining balance is a rather complicated global optimum problem for a quadruped robot which is about seeking a foot contact point prevents itself from falling down. To ensure the stability of the intelligent robot control system, the intelligent optimization method is employed. The proposed chaotic artificial bee colony (CABC) algorithm is based on basic ABC, and a chaotic mechanism is used to help the algorithm to jump out of the local optimum as well as finding the optimal parameters. The implementation procedure of our proposed chaotic ABC approach is described in detail.

Findings

The proposed CABC method is applied to a quadruped robot in ADAMS simulator. Using the CABC to implement, the quadruped robot can work smoothly under the interference. A comparison among the basic ABC and CABC is made. Experimental results verify a better trajectory tracking response can be achieved by the proposed CABC method after control parameters training.

Practical implications

The proposed CABC algorithm can be easily applied to practice and can steer the robot during walking, which will considerably increase the autonomy of the robot.

Originality/value

The proposed CABC approach is interesting for the optimization of a control scheme for quadruped robot. A parameter training methodology, using the presented intelligent algorithm is proposed to increase the learning capability. The experimental results verify the system stabilization, favorable performance and no chattering phenomena can be achieved by using the proposed CABC algorithm. And, the proposed CABC methodology can be easily extended to other applications.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 7 no. 2
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 6 March 2017

Soyinka Olukunle Kolawole and Duan Haibin

Keeping satellite position within close tolerances is key for the utilization of satellite formations for space missions. The presence of perturbation forces makes control…

Abstract

Purpose

Keeping satellite position within close tolerances is key for the utilization of satellite formations for space missions. The presence of perturbation forces makes control inevitable if such mission objective is to be realised. Various approaches have been used to obtain feedback controller parameters for satellites in a formation; this paper aims to approach the problem of estimating the optimal feedback parameter for a leader–follower pair of satellites in a small eccentric orbit using nature-based search algorithms.

Design/methodology/approach

The chaotic artificial bee colony algorithm is a variant of the basic artificial bee colony algorithm. The algorithm mimics the behaviour of bees in their search for food sources. This paper uses the algorithm in optimizing feedback controller parameters for a satellite formation control problem. The problem is formulated to optimize the controller parameters while minimizing a fuel- and state-dependent cost function. The dynamical model of the satellite is based on Gauss variational equations with J2 perturbation. Detailed implementation of the procedure is provided, and experimental results of using the algorithm are also presented to show feasibility of the method.

Findings

The experimental results indicate the feasibility of this approach, clearly showing the effective control of the transients that arise because of J2 perturbation.

Originality/value

This paper applied a swarm intelligence approach to the problem of estimating optimal feedback control parameter for a pair of satellites in a formation.

Details

Aircraft Engineering and Aerospace Technology, vol. 89 no. 2
Type: Research Article
ISSN: 1748-8842

Keywords

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Article
Publication date: 4 December 2020

Yuquan Wang and Naiming Xie

purpose of this paper is providing a solution for flexible flow shop scheduling problem with uncertain processing time in aeronautical composite lay-up workshop.

Abstract

Purpose

purpose of this paper is providing a solution for flexible flow shop scheduling problem with uncertain processing time in aeronautical composite lay-up workshop.

Design/methodology/approach

A flexible flow scheduling model and algorithm with interval grey processing time is established. First, according to actual needs of composite laminate shop scheduling process, interval grey number is used to represent uncertain processing time, and interval grey processing time measurement method, grey number calculation and comparison rules, grey Gantt chart, and other methods are further applied. Then a flexible flow shop scheduling model with interval grey processing time (G-FFSP) is established, and an artificial bee colony algorithm based on an adaptive neighbourhood search strategy is designed to solve the model. Finally, six examples are generated for simulation scheduling, and the efficiency and performance of the model and algorithm are evaluated by comparing the results.

Findings

Results show that flexible flow shop scheduling model and algorithm with interval grey processing time can provide an optimal solution for composite lay-up shop scheduling problems and other similar flow shop scheduling problems.

Social implications

Uncertain processing time is common in flexible workshop manufacturing, and manual scheduling greatly restricts the production efficiency of workshop. In this paper, combined with grey system theory, an intelligent algorithm is used to solve flexible flow shop scheduling problem to promote intelligent and efficient production of enterprises.

Originality/value

This paper applies and perfects interval grey processing time measurement method, grey number calculation and comparison rules, grey Gantt chart and other methods. A flexible flow shop scheduling model with interval grey processing time is established, and an artificial bee colony algorithm with an adaptive domain search strategy is designed. It provides a comprehensive solution for flexible flow shop scheduling with uncertain processing time.

Details

Grey Systems: Theory and Application, vol. 11 no. 4
Type: Research Article
ISSN: 2043-9377

Keywords

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Article
Publication date: 23 August 2019

Janani Balakumar and S. Vijayarani Mohan

Owing to the huge volume of documents available on the internet, text classification becomes a necessary task to handle these documents. To achieve optimal text…

Abstract

Purpose

Owing to the huge volume of documents available on the internet, text classification becomes a necessary task to handle these documents. To achieve optimal text classification results, feature selection, an important stage, is used to curtail the dimensionality of text documents by choosing suitable features. The main purpose of this research work is to classify the personal computer documents based on their content.

Design/methodology/approach

This paper proposes a new algorithm for feature selection based on artificial bee colony (ABCFS) to enhance the text classification accuracy. The proposed algorithm (ABCFS) is scrutinized with the real and benchmark data sets, which is contrary to the other existing feature selection approaches such as information gain and χ2 statistic. To justify the efficiency of the proposed algorithm, the support vector machine (SVM) and improved SVM classifier are used in this paper.

Findings

The experiment was conducted on real and benchmark data sets. The real data set was collected in the form of documents that were stored in the personal computer, and the benchmark data set was collected from Reuters and 20 Newsgroups corpus. The results prove the performance of the proposed feature selection algorithm by enhancing the text document classification accuracy.

Originality/value

This paper proposes a new ABCFS algorithm for feature selection, evaluates the efficiency of the ABCFS algorithm and improves the support vector machine. In this paper, the ABCFS algorithm is used to select the features from text (unstructured) documents. Although, there is no text feature selection algorithm in the existing work, the ABCFS algorithm is used to select the data (structured) features. The proposed algorithm will classify the documents automatically based on their content.

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Article
Publication date: 7 July 2021

Habib Shah

Breast cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications.

Abstract

Purpose

Breast cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications.

Design/methodology/approach

The new artificial bee colony (ABC) implementation has been applied to probabilistic neural network (PNN) for training and testing purpose to classify the breast cancer data set.

Findings

The new ABC algorithm along with PNN has been successfully applied to breast cancers data set for prediction purpose with minimum iteration consuming.

Originality/value

The new implementation of ABC along PNN can be easily applied to times series problems for accurate prediction or classification.

Details

Frontiers in Engineering and Built Environment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-2499

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Article
Publication date: 12 June 2017

Dalian Yang, Yilun Liu, Songbai Li, Jie Tao, Chi Liu and Jiuhuo Yi

The aim of this paper is to solve the problem of low accuracy of traditional fatigue crack growth (FCG) prediction methods.

Abstract

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.

Details

Engineering Computations, vol. 34 no. 4
Type: Research Article
ISSN: 0264-4401

Keywords

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