Hybrid clonal selection algorithm and the artificial bee colony algorithm for a variable PID-like fuzzy controller design

2012 ◽  
Author(s):  
Jia-Ping Tien ◽  
Tzuu-Hseng S. Li
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Clonal Selection ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Bee Colony

scholarly journals Shortest Path Selection Algorithm for Cold Chain Logistics Transportation Based on Improved Artificial Bee Colony

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhihuan Liu
Keyword(s):  
Shortest Path ◽  
Artificial Bee Colony Algorithm ◽  
Food Source ◽  
Artificial Bee Colony ◽  
Selection Effect ◽  
Path Selection ◽  
Cold Chain ◽  
Selection Algorithm ◽  
Selection Accuracy ◽  
Bee Colony

Aiming at the problems of low shortest path selection accuracy, longer response time, and poor selection effect in current cold chain logistics transportation methods, a cold chain logistics transportation shortest path selection algorithm based on improved artificial bee colony is proposed. The improved algorithm is used to initialize the food source, reevaluate the fitness value of the food source, generate a new food source, optimize the objective function and food source evaluation strategy, and get an improved artificial bee colony algorithm. Based on the improved artificial bee colony algorithm, the group adaptive mechanism of particle swarm algorithm is introduced to initialize the position and velocity of each particle randomly. Dynamic detection factor and octree algorithm are adopted to dynamically update the path of modeling environment information. According to the information sharing mechanism between individual particles, the group adaptive behavior control is performed. After the maximum number of cycles, the path planning is completed, the shortest path is output, and the shortest path selection of cold chain logistics transportation is realized. The experimental results show that the shortest path selection effect of the cold chain logistics transportation of the proposed algorithm is better, which can effectively improve the shortest path selection accuracy and reduce the shortest path selection time.

scholarly journals Optimal PID controller design using artificial bee colony algorithm for robot arm

2021 ◽  
Vol 21 (1) ◽  
pp. 84
Author(s):  
Ghassan A. Sultan ◽  
Muhammed K. Jarjes
Keyword(s):  
Pid Controller ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Controller Design ◽  
Dynamic Performance ◽  
Control Process ◽  
Robot Arm ◽  
Bee Colony ◽  
Pid Controller Design ◽  
Best Parameter

<p class="Default"><span>Proportional integral derivation (PID) controller is used in this paper for optimal design, and tuning by zeigler and nichol (ZN) with artificial bee colony algorithm. The best parameter were found using these algorithms for best performance of a robot arm. The advantage of using ABC were highlighted. The controller using the new algorithm was tested for valid control process. Different colony size has been performed for tuning process, settling time, from time domain performance, rise time, overshot, and steady state error with ABC tuning give better dynamic performance than controller using the (ZN).</span></p>

scholarly journals Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Ramsha Rizwan ◽  
Farrukh Aslam Khan ◽  
Haider Abbas ◽  
Sajjad Hussain Chauhdary
Keyword(s):  
Clonal Selection ◽  
Wireless Sensor ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Biologically Inspired ◽  
Data Packets ◽  
Negative Selection Algorithm ◽  
Bee Colony ◽  
Anomalous Data ◽  
Network Intrusion

During the past few years, we have seen a tremendous increase in various kinds of anomalies in Wireless Sensor Network (WSN) communication. Recently, researchers have shown a lot of interest in applying biologically inspired systems for solving network intrusion detection problems. Several solutions have been proposed using Artificial Immune System (AIS), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and so forth. In this paper, we propose a bioinspired solution using Negative Selection Algorithm (NSA) of the AIS for anomalies detection in WSNs. For this purpose, we implement the enhanced NSA and make a detector set that holds anomalous packets only. Then the random packets are tested and matched with the detector set and anomalies are identified. Anomalous data packets are used for further processing to identify specific anomalies. In this way, the number of wormholes, packets delayed, and packets dropped are calculated and identified. Simulations are performed on a large dataset and the results show high accuracy of the proposed algorithm in detecting anomalies. The proposed NSA is also compared with Clonal Selection Algorithm (CSA) for the same dataset. The results show significant improvement of the proposed NSA over CSA in most of the cases.

scholarly journals The application of proportional–integral–derivative–type fuzzy controller for synchronized XY motion gantry stage system

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401775279 ◽  
Author(s):  
Wei-Lung Mao ◽  
Chung-Wen Hung ◽  
Suprapto
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Transfer Functions ◽  
Fuzzy Controller ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Bee Colony ◽  
Wide Range ◽  
Derivative Type ◽  
Stage System

Gantry systems in which two linear motors are used to drive a single axis provide flexible and efficient solutions for a wide range of material handling applications. It is an important issue to find a way to drive the parallel stage to achieve a synchronous motion effectively and precisely. In this research, the proportional–integral–derivative–type fuzzy controller structure is presented for precision trajectory tracking control in synchronized XY motion gantry stage system. Three proportional–integral–derivative–type fuzzy controllers are designed for each axis, and the complete membership functions and rule table are developed to fulfill the better tracking capability. The controller parameters, which include the scaling factor of fuzzy rules and proportional–integral structures, are searched using the cross-mixing global artificial bee colony algorithm. The algorithm can optimize these parameters based on the integral of the time-weighted absolute error criterion. MATLAB system identification tool is used to find the equivalent coupled transfer functions of the gantry system. The proposed cross-mixing global artificial bee colony algorithm is utilized to offer the better convergence speed and avoid the local optimal solution in the searching process. The simulation results and experimental results on star and circle reference contours are presented to show that the proposed cross-mixing global artificial bee colony–based fuzzy controller indeed accomplish the better tracking performances in motion control application.

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