Artificial bee colony–based Kalman filter hybridization for three–dimensional position estimation of a quadrotor

2020 ◽  
Vol 92 (10) ◽  
pp. 1523-1532 ◽  
Author(s):  
Aziz Kaba ◽  
Emre Kiyak
Keyword(s):  
Kalman Filter ◽  
Objective Function ◽  
Artificial Bee Colony ◽  
Mathematical Proof ◽  
Position Estimation ◽  
Measurement Noise ◽  
Content Type ◽  
Penalty Term ◽  
Bee Colony ◽  
Noise Statistics

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.

Design of parallel adaptive extended Kalman filter for online estimation of noise covariance

2018 ◽  
Vol 91 (1) ◽  
pp. 112-123 ◽  
Author(s):  
Kai Xiong ◽  
Liangdong Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Position Estimation ◽  
Measurement Noise ◽  
Estimation Accuracy ◽  
Final State ◽  
Content Type ◽  
Noise Covariance ◽  
Adaptive Extended Kalman Filter ◽  
Noise Statistics

Purpose The successful use of the standard extended Kalman filter (EKF) is restricted by the requirement on the statistics information of the measurement noise. The covariance of the measurement noise may deviate from its nominal value in practical environment, and the filtering performance may decline because of the statistical uncertainty. Although the adaptive EKF (AEKF) is available for recursive covariance estimation, it is often less accurate than the EKF with accurate noise statistics. Design/methodology/approach Aiming at this problem, this paper develops a parallel adaptive EKF (PAEKF) by combining the EKF and the AEKF with an adaptive law, such that the final state estimate is dominated by the EKF when the prior noise covariance is accurate, while the AEKF is activated when the actual noise covariance deviates from its nominal value. Findings The PAEKF can reduce the sensitivity of the algorithm to the model uncertainty and ensure the estimation accuracy in the normal case. The simulation results demonstrate that the PAEKF has the advantage of both the AEKF and the EKF. Practical implications The presented algorithm is applicable for spacecraft relative attitude and position estimation. Originality/value The PAEKF is presented for a kind of nonlinear uncertain systems. Stability analysis is provided to show that the error of the estimator is bounded under certain assumptions.

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.

scholarly journals Green Product Module Partition Method Based on Improved Multi-objective Artificial Bee Colony Algorithm

2019 ◽  
Vol 301 ◽  
pp. 00021
Author(s):  
Wei Wei ◽  
Yang Zhan
Keyword(s):  
Life Cycle ◽  
Objective Function ◽  
Correlation Matrix ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Green Product ◽  
Multi Objective ◽  
Bee Colony ◽  
Axiomatic Design Theory ◽  
Module Partition

Modular design is an important design method in the mass customization for manufacturing industry. The purpose of this paper is to meet diverse market demands while reducing the impact of products on the ecological environment. Firstly, aiming at the product life cycle process, this paper summarizes the problems encountered in each stage of the product, and introduces five green product module partition principles. Then, through the component correlation matrix, the resource greenness objective function based on the whole life cycle and the polymerization degree objective function based on the component correlation matrix are established respectively by the axiomatic design theory which makes the product mapping from functional domain to structural domain. Next, an improved artificial bee colony algorithm is proposed. Based on the artificial bee colony algorithm, the algorithm applies congestion strategy and fast nondominated sorting strategy to solve the module partition problem of product platform with multi-objective optimization, and a uniformly distributed pare to solution set is generated. Through above steps, the optimization results of module partition are obtained. Finally, an application example of aircraft tail horizontal stabilizer parts is given, and the advantages of the algorithm are proved by comparing with other algorithms.

scholarly journals Adaptive Dual Extended Kalman Filter Based on Variational Bayesian Approximation for Joint Estimation of Lithium-Ion Battery State of Charge and Model Parameters

2019 ◽  
Vol 9 (9) ◽  
pp. 1726 ◽  
Author(s):  
Jing Hou ◽  
Yan Yang ◽  
He He ◽  
Tian Gao
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
Measurement Noise ◽  
Estimation Accuracy ◽  
Variational Bayesian ◽  
Soc Estimation ◽  
Bayesian Approximation ◽  
Noise Statistics ◽  
Discharge Test

An accurate state of charge (SOC) estimation is vital for the safe operation and efficient management of lithium-ion batteries. At present, the extended Kalman filter (EKF) can accurately estimate the SOC under the condition of a precise battery model and deterministic noise statistics. However, in practical applications, the battery characteristics change with different operating conditions and the measurement noise statistics may vary with time, resulting in nonoptimal and even unreliable estimation of SOC by EKF. To improve the SOC estimation accuracy under uncertain measurement noise statistics, a variational Bayesian approximation-based adaptive dual extended Kalman filter (VB-ADEKF) is proposed in this paper. The variational Bayesian inference is integrated with the dual EKF (DEKF) to jointly estimate the lithium-ion battery parameters and SOC. Meanwhile, the measurement noise variances are simultaneously estimated in the SOC estimation process to compensate for the model uncertainties, so that the adaptability of the proposed algorithm to dynamic changes in battery characteristics is greatly improved. A constant current discharge test, a pulse current discharge test, and an urban dynamometer driving schedule (UDDS) test are performed to verify the effectiveness and superiority of the proposed algorithm by comparison with the DEKF algorithm. The experimental results show that the proposed VB-ADEKF algorithm outperforms the traditional DEKF algorithm in terms of SOC estimation accuracy, convergence rate, and robustness.

Satellite formation keeping via chaotic artificial bee colony

2017 ◽  
Vol 89 (2) ◽  
pp. 246-256 ◽  
Author(s):  
Soyinka Olukunle Kolawole ◽  
Duan Haibin
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Feedback Controller ◽  
Experimental Results ◽  
Effective Control ◽  
Satellite Formation ◽  
Content Type ◽  
Optimal Feedback ◽  
Bee Colony ◽  
J2 Perturbation

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.

A comparative study of population-based algorithms for a political districting problem

Kybernetes ◽  
2017 ◽  
Vol 46 (1) ◽  
pp. 172-190 ◽  
Author(s):  
Eric Alfredo Rincón-García ◽  
Miguel Ángel Gutiérrez-Andrade ◽  
Sergio Gerardo de-los-Cobos-Silva ◽  
Roman Anselmo Mora-Gutiérrez ◽  
Antonin Ponsich ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Artificial Bee Colony ◽  
Population Based ◽  
Efficient Solutions ◽  
Computational Experiments ◽  
Musical Composition ◽  
Solution Quality ◽  
Content Type ◽  
Weighting Factors ◽  
Bee Colony

Purpose This paper aims to propose comparing the performance of three algorithms based on different population-based heuristics, particle swarm optimization (PSO), artificial bee colony (ABC) and method of musical composition (DMMC), for the districting problem. Design/methodology/approach In order to compare the performance of the proposed algorithms, they were tested on eight instances drawn from the Mexican electoral institute database, and their respective performance levels were compared. In addition, a simulated annealing-based (simulated annealing – SA) algorithm was used as reference to evaluate the proposed algorithms. This technique was included in this work because it has been used for Federal districting in Mexico since 1994. The performance of the algorithms was evaluated in terms of the quality of the approximated Pareto front and efficiency. Regarding solution quality, convergence and dispersion of the resulting non-dominated solutions were evaluated. Findings The results show that the quality and diversification of non-dominated solutions generated by population-based algorithms are better than those produced by Federal Electoral Institute’s (IFE’s) SA-based technique. More accurately, among population-based techniques, discrete adaptation of ABC and MMC outperform PSO. Originality/value The performance of three population-based techniques was evaluated for the districting problem. In this paper, the authors used the objective function proposed by the Mexican IFE, a weight aggregation function that seeks for a districting plan that represents the best balance between population equality and compactness. However, the weighting factors can be modified by political agreements; thus, the authors decided to produce a set of efficient solutions, using different weighting factors for the computational experiments. This way, the best algorithm will produce high quality solutions no matter the weighting factors used for a real districting process. The computational experiments proved that the proposed artificial bee colony and method of musical composition-based algorithms produce better quality efficient solutions than its counterparts. These results show that population-based algorithms can outperform traditional local search strategies. Besides, as far as we know, this is the first time that the method of musical composition is used for this kind of problems.

scholarly journals Using new artificial bee colony as probabilistic neural network for breast cancer data classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Habib Shah
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Bee Colony ◽  
Probabilistic Neural Network ◽  
Breast Cancers ◽  
Breast Cancer Data ◽  
Data Set ◽  
Content Type ◽  
Cancer Data ◽  
Bee Colony

PurposeBreast cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications.Design/methodology/approachThe 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.FindingsThe new ABC algorithm along with PNN has been successfully applied to breast cancers data set for prediction purpose with minimum iteration consuming.Originality/valueThe new implementation of ABC along PNN can be easily applied to times series problems for accurate prediction or classification.

scholarly journals Adaptive information sharing approach for crowd networks based on two stage optimization

2019 ◽  
Vol 3 (3) ◽  
pp. 284-302
Author(s):  
Xiaoni Wang ◽  
Zhiwen Pan ◽  
Zhouxia Li ◽  
Wen Ji ◽  
Feng Yang
Keyword(s):  
Objective Function ◽  
Information Sharing ◽  
Network Topology ◽  
Heuristic Algorithms ◽  
Dynamic Change ◽  
Task Completion ◽  
Content Type ◽  
Bee Colony ◽  
Information Demand ◽  
Sharing Patterns

Purpose This paper aims to optimize and evaluating the performance of the crowd networks through analyzing their information sharing patterns. That is, in a crowd network, the qualities of accomplishing tasks are highly dependent on the effective information sharing among intelligent subjects within the networks. Hence, proposing an adaptive information-sharing approach can help improve the performance of crowd networks on accomplishing tasks that are assigned to them. Design/methodology/approach This paper first introduces the factors that affect effectiveness of information-sharing pattern: the network topology, the resources owned by intelligent subjects and the degree of information demand. By analyzing the correlation between these factors and the performance of crowd networks, an Adaptive Information Sharing Approach for Crowd Networks (AISCN approach) is proposed. By referring to information needed for accomplishing the historical tasks that are assigned to a crowd network, the AISCN approach can explore the optimized information-sharing pattern based on the predefined composite objective function. The authors implement their approach on two crowd networks including bee colony and supply chain, to prove the effectiveness of the approach. Findings The shared information among intelligent subjects affects the efficiency of task completion in the crowd network. The factors that can be used to describe the effectiveness of information-sharing patterns include the network topology, the resources owned by intelligent subjects and the degree of information demand. The AISCN approach used heuristic algorithm to solve a composite objective function which takes all these factors into consideration, so that the optimized information-sharing pattern can be obtained. Originality/value This paper introduces a set of factors that can be used to describe the correlation between information-sharing pattern and performance of crowd network. By quantifying such correlation based on these factors, this paper proposes an adaptive information-sharing approach which can explore the optimized information-sharing pattern for a variety of crowd networks. As the approach is a data-driven approach that explores the information-sharing pattern based on the network’s performance on historical tasks and network’s characteristics, it is adaptive to the dynamic change (change of incoming tasks, change of network characteristics) of the target crowd network. To ensure the commonality of the information-sharing approach, the proposed approach is not designed for a specific optimization algorithm. In this way, during the implementation of the proposed approach, heuristic algorithms can be chosen according to the complexity of the target crowd network.

Metode Hibridasi Artificial Bee Colony dan Fuzzy K-Modes untuk Klasterisasi Data Kategorikal

2019 ◽  
Vol 4 (2) ◽  
pp. 36-42
Author(s):  
Khalid Khalid
Keyword(s):  
Breast Cancer ◽  
Objective Function ◽  
Artificial Bee Colony ◽  
Dissimilarity Measure ◽  
Data Repository ◽  
Congressional Voting ◽  
Bee Colony ◽  
F Measure

Fuzzy K-Modes (FKMO) merupakan metode klasterisasi data yang efektif untuk data kategorikal. Metode ini menggunakan metode fuzzy dan pencocokan ukuran ketidaksamaan (dissimilarity measure) yang sederhana untuk memutakhirkan titik pusat klaster dan mendapatkan solusi yang optimal. Meskipun demikian Fuzzy K-Modes memiliki kelemahan adanya kemungkinan berhenti dalam solusi lokal optimal. Artificial Bee Colony (ABC) merupakan metode optimasi yang efektif dan terbukti memiliki kemampuan mendapatkan solusi global. Penelitian ini mengusulkan penggunaan algoritma Artificial Bee Colony untuk melakukan optimasi terhadap Fuzzy K-Modes untuk klasterisasi data kategorikal (ABC-FKMO).  Implementasi Artifical Bee Colony untuk optimasi Fuzzy K-Modes terbukti mampu meningkatkan performa klasterisasi data kategorikal khususnya dalam aspek nilai Objective Function, F-Measure, dan Accuracy. Hasil pengujian dengan  dataset Soybean Disease, Breast Cancer dan Congressional Voting Records dari UCI data repository, menunjukkan rata-rata accuracy sebesar 0.991, 0.615, dan 0.867. Objective Function lebih baik rata rata sebesar 2,73 %, F-Measure lebih baik rata-rata sebesar 4,31 % dan Accuracy lebih baik rata-rata sebesar 5,16 %.

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