scholarly journals Chaotic Artificial Bee Colony Algorithm for System Identification of a Small-Scale Unmanned Helicopter

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Li Ding ◽  
Hongtao Wu ◽  
Yu Yao
Keyword(s):  
System Identification ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
State Space Model ◽  
Global Optimum ◽  
Small Scale ◽  
Local Optimum ◽  
Unmanned Helicopter ◽  
Bee Colony ◽  
Chaotic Operator

The purpose of this paper is devoted to developing a chaotic artificial bee colony algorithm (CABC) for the system identification of a small-scale unmanned helicopter state-space model in hover condition. In order to avoid the premature of traditional artificial bee colony algorithm (ABC), which is stuck in local optimum and can not reach the global optimum, a novel chaotic operator with the characteristics of ergodicity and irregularity was introduced to enhance its performance. With input-output data collected from actual flight experiments, the identification results showed the superiority of CABC over the ABC and the genetic algorithm (GA). Simulations are presented to demonstrate the effectiveness of our proposed algorithm and the accuracy of the identified helicopter model.

scholarly journals Modelling of Horizontal Flexible Plate Structure Using Artificial Bee Colony Algorithm

2018 ◽  
Vol 7 (4.36) ◽  
pp. 415
Author(s):  
Muhamad Sukri Hadi ◽  
Sukri Hadi Zaurah Mat Darus
Keyword(s):  
System Identification ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Mean Squared Error ◽  
Least Square ◽  
Flexible Plate ◽  
Plate Structure ◽  
Bee Colony ◽  
Squared Error ◽  
Plate System

This paper presents the performance of system identification for modeling the horizontal flexible plate system using artificial bee colony and recursive least square algorithms. Initially, the experimental rig of flexible plate was designed and fabricated with all edges clamped boundary condition at the horizontal position. Then, the instrumentation and data acquisition systems were integrated into the rig for acquiring the input-output vibration experimentally. The collected data in the experiment will be used later for modeling the dynamic system of horizontal flexible plate system using system identification. The effectiveness of the developed model will be validated using mean squared error, one step ahead prediction, correlation tests and pole zero diagram stability. The estimated of the developed models were found are acceptable and possible to be used as a platform of controller development for vibration suppression of the undesirable vibration in the flexible plate structure. It was found that the artificial bee colony algorithm has performed better in this study by achieving the lowest mean squared error, good correlation test and high stability in the pole zero diagram.  

scholarly journals 15-Puzzle Problem Solving with the Artificial Bee Colony Algorithm Based on Pattern Database

2021 ◽  
Vol 27 (6) ◽  
pp. 635-645
Author(s):  
Adem Tuncer
Keyword(s):  
Artificial Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Algorithms ◽  
Manhattan Distance ◽  
Small Scale ◽  
Heuristic Function ◽  
New Approach ◽  
Abc Algorithm ◽  
Bee Colony

The N-puzzle problem is one of the most classical problems in mathematics. Since the number of states in the N-puzzle is equal to the factorial of the number of tiles, traditional algorithms can only provide solutions for small-scale ones, such as 8-puzzle. Various uninformed and informed search algorithms have been applied to solve the N-puzzle, and their performances have been evaluated. Apart from traditional methods, artificial intelligence algorithms are also used for solutions. This paper introduces a new approach based on a meta-heuristic algorithm with a solving of the 15-puzzle problem. Generally, only Manhattan distance is used as the heuristic function, while in this study, a linear conflict function is used to increase the effectiveness of the heuristic function. Besides, the puzzle was divided into subsets named pattern database, and solutions were obtained for the subsets separately with the artificial bee colony (ABC) algorithm. The proposed approach reveals that the ABC algorithm is very successful in solving the 15-puzzle problem.

Sentiment Analysis on Microblogging with K-Means Clustering and Artificial Bee Colony

2019 ◽  
Vol 18 (03) ◽  
pp. 1950017 ◽  
Author(s):  
Korawit Orkphol ◽  
Wu Yang
Keyword(s):  
Artificial Bee Colony ◽  
Clustering Algorithm ◽  
Global Optimum ◽  
High Dimensional ◽  
Local Optimum ◽  
Short Message ◽  
Initial State ◽  
Bee Colony ◽  
Analysis Technique ◽  
Value Decomposition

Microblogging is a type of blog used by people to express their opinions, attitudes, and feelings toward entities with a short message and this message is easily shared through the network of connected people. Knowing their sentiments would be beneficial for decision-making, planning, visualization, and so on. Grouping similar microblogging messages can convey some meaningful sentiments toward an entity. This task can be accomplished by using a simple and fast clustering algorithm, [Formula: see text]-means. As the microblogging messages are short and noisy they cause high sparseness and high-dimensional dataset. To overcome this problem, term frequency–inverse document frequency (tf–idf) technique is employed for selecting the relevant features, and singular value decomposition (SVD) technique is employed for reducing the high-dimensional dataset while still retaining the most relevant features. These two techniques adjust dataset to improve the [Formula: see text]-means efficiently. Another problem comes from [Formula: see text]-means itself. [Formula: see text]-means result relies on the initial state of centroids, the random initial state of centroids usually causes convergence to a local optimum. To find a global optimum, artificial bee colony (ABC), a novel swarm intelligence algorithm, is employed to find the best initial state of centroids. Silhouette analysis technique is also used to find optimal [Formula: see text]. After clustering into [Formula: see text] groups, each group will be scored by SentiWordNet and we analyzed the sentiment polarities of each group. Our approach shows that combining various techniques (i.e., tf–idf, SVD, and ABC) can significantly improve [Formula: see text]-means result (41% from normal [Formula: see text]-means).

scholarly journals Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation

2021 ◽  
Vol 11 (5) ◽  
pp. 4050
Author(s):  
Waleed Alomoush ◽  
Ayat Alrosan ◽  
Ammar Almomani ◽  
Khalid Alissa ◽  
Osama A. Khashan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Medical Image Segmentation ◽  
Local Optimum ◽  
Brain Image ◽  
Bee Colony

Fuzzy c-means algorithm (FCM) is among the most commonly used in the medical image segmentation process. Nevertheless, the traditional FCM clustering approach has been several weaknesses such as noise sensitivity and stuck in local optimum, due to FCM hasn’t able to consider the information of contextual. To solve FCM problems, this paper presented spatial information of fuzzy clustering-based mean best artificial bee colony algorithm, which is called SFCM-MeanABC. This proposed approach is used contextual information in the spatial fuzzy clustering algorithm to reduce sensitivity to noise and its used MeanABC capability of balancing between exploration and exploitation that is explore the positive and negative directions in search space to find the best solutions, which leads to avoiding stuck in a local optimum. The experiments are carried out on two kinds of brain images the Phantom MRI brain image with a different level of noise and simulated image. The performance of the SFCM-MeanABC approach shows promising results compared with SFCM-ABC and other stats of the arts.

scholarly journals Multiobjective Cognitive Cooperative Jamming Decision-Making Method Based on Tabu Search-Artificial Bee Colony Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Fang Ye ◽  
Fei Che ◽  
Lipeng Gao
Keyword(s):  
Tabu Search ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Local Optimum ◽  
Tabu List ◽  
Cooperative Jamming ◽  
Bee Colony ◽  
Local Optimal Solution ◽  
The One

For the future information confrontation, a single jamming mode is not effective due to the complex electromagnetic environment. Selecting the appropriate jamming decision to coordinately allocate the jamming resources is the development direction of the electronic countermeasures. Most of the existing studies about jamming decision only pay attention to the jamming benefits, while ignoring the jamming cost. In addition, the conventional artificial bee colony algorithm takes too many iterations, and the improved ant colony (IAC) algorithm is easy to fall into the local optimal solution. Against the issue, this paper introduces the concept of jamming cost in the cognitive collaborative jamming decision model and refines it as a multiobjective one. Furthermore, this paper proposes a tabu search-artificial bee colony (TSABC) algorithm to cognitive cooperative-jamming decision. It introduces the tabu list into the artificial bee colony (ABC) algorithm and stores the solution that has not been updated after a certain number of searches into the tabu list to avoid meeting them when generating a new solution, so that this algorithm reduces the unnecessary iterative process, and it is not easy to fall into a local optimum. Simulation results show that the search ability and probability of finding the optimal solution of the new algorithm are better than the other two. It has better robustness, which is better in the “one-to-many” jamming mode.

Artificial bee colony optimised controller for small-scale unmanned helicopter

2017 ◽  
Vol 121 (1246) ◽  
pp. 1879-1896 ◽  
Author(s):  
R. Ma ◽  
H. Wu ◽  
L. Ding
Keyword(s):  
Attitude Control ◽  
Artificial Bee Colony ◽  
Control Method ◽  
Hierarchical Control ◽  
Small Scale ◽  
Unmanned Helicopter ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Bee Colony ◽  
Loop Transfer Recovery

ABSTRACTIn this paper, an efficient approach to design and optimize a flight controller of a small-scale unmanned helicopter is proposed. Given the identified helicopter model, the Linear Quadratic Gaussian/Loop Transfer Recovery (LQG/LTR) robust control method is applied for trajectory tracking and attitude control of the helicopter with a two-loop hierarchical control architecture. Since the performance of the controller extremely depends on its weighting matrices, the Artificial Bee Colony (ABC) algorithm is introduced to automatically select the parameters of the matrices. Comparative studies between optimal algorithms are also carried out. A series of flight experiments and simulations are conducted to investigate the effectiveness and robustness of the proposed optimised controller.

Hybrid Artificial Bee Colony Algorithm with Differential Evolution and Free Search for Numerical Function Optimization

2016 ◽  
Vol 25 (04) ◽  
pp. 1650020 ◽  
Author(s):  
Lian Lian ◽  
Fu Zaifeng ◽  
Yang Guangfei ◽  
Huang Yi
Keyword(s):  
Differential Evolution ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Robustness Analysis ◽  
Selection Strategy ◽  
Local Optimum ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Free Search

Artificial bee colony (ABC) algorithm invented by Karaboga has been proved to be an efficient technique compared with other biological-inspired algorithms for solving numerical optimization problems. Unfortunately, convergence speed of ABC is slow when working with certain optimization problems and some complex multimodal problems. Aiming at the shortcomings, a hybrid artificial bee colony algorithm is proposed in this paper. In the hybrid ABC, an improved search operator learned from Differential Evolution (DE) is applied to enhance search process, and a not-so-good solutions selection strategy inspired by free search algorithm (FS) is introduced to avoid local optimum. Especially, a reverse selection strategy is also employed to do improvement in onlooker bee phase. In addition, chaotic systems based on the tent map are executed in population initialization and scout bee's phase. The proposed algorithm is conducted on a set of 40 optimization test functions with different mathematical characteristics. The numerical results of the data analysis, statistical analysis, robustness analysis and the comparisons with other state-of-the-art-algorithms demonstrate that the proposed hybrid ABC algorithm provides excellent convergence and global search ability.

scholarly journals Design of intrusion detection system based on improved ABC_elite and BP neural networks

2019 ◽  
Vol 16 (3) ◽  
pp. 773-795
Author(s):  
Letian Duan ◽  
Dezhi Han ◽  
Qiuting Tian
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Detection System ◽  
Local Optimum ◽  
Bp Neural Networks ◽  
Guided Search ◽  
Bee Colony

Intrusion detection is a hot topic in network security. This paper proposes an intrusion detection method based on improved artificial bee colony algorithm with elite-guided search equations (IABC elite) and Backprogation (BP) neural net works. The IABC elite algorithm is based on the depth first search framework and the elite-guided search equations, which enhance the exploitation ability of artificial bee colony algorithm and accelerate the convergence. The IABC elite algorithm is used to optimize the initial weight and threshold value of the BP neural networks, avoiding the BP neural networks falling into a local optimum during the training process and improving the training speed. In this paper, the BP neural networks optimized by IABC elite algorithm is applied to intrusion detection. The simulation on the NSL-KDD dataset shows that the intrusion detection system based on the IABC elite algorithm and the BP neural networks has good classification and high intrusion detection ability.

scholarly journals Dynamical Model Identification for a Small-Scale Unmanned Helicopter Using an Integrated Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Rui Ma ◽  
Li Ding ◽  
Kailei Liu ◽  
Hongtao Wu
Keyword(s):  
Model Identification ◽  
Wind Tunnel Test ◽  
Integrated Approach ◽  
Dynamical Model ◽  
Small Scale ◽  
Unmanned Helicopter ◽  
Bee Colony ◽  
Chaotic Operator ◽  
Dynamical Parameters ◽  
Search Speed

This article presents an integrated approach for the parameter identification of a small-scale unmanned helicopter. With the flight experiment data collection and preprocessing, a hybrid identified algorithm combining the improved artificial bee colony algorithm and prediction error method is proposed to obtain the unknown dynamical parameters of the linear model. The proposed algorithm is valid to use thanks to an adaptive search equation, a novel probability-scaling method, and a chaotic operator and has a good performance in search speed and quality. Afterwards, we design a wind tunnel test to modify the main rotor time constant of the identified model. The identified accuracy and feasibility of the proposed approach are verified by making a time-domain comparison with three other algorithms. Results show that the dynamical characteristics of the helicopter can be determined accurately by the identified model. And the proposed approach is propitious to enhance the reliability and availability of the identified dynamical model.

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