Parameter identification of piezoelectric hysteresis model based on improved artificial bee colony algorithm

2018 ◽  
Vol 32 (11) ◽  
pp. 1850131 ◽  
Author(s):  
Geng Wang ◽  
Kexin Zhou ◽  
Yeming Zhang
Keyword(s):  
Food Source ◽  
Artificial Bee Colony ◽  
Search Performance ◽  
Hysteresis Model ◽  
Unknown Parameters ◽  
Optimal Position ◽  
Bee Colony ◽  
Standard Particle Swarm Optimization ◽  
Global Exploitation ◽  
Better Than

The widely used Bouc–Wen hysteresis model can be utilized to accurately simulate the voltage–displacement curves of piezoelectric actuators. In order to identify the unknown parameters of the Bouc–Wen model, an improved artificial bee colony (IABC) algorithm is proposed in this paper. A guiding strategy for searching the current optimal position of the food source is proposed in the method, which can help balance the local search ability and global exploitation capability. And the formula for the scout bees to search for the food source is modified to increase the convergence speed. Some experiments were conducted to verify the effectiveness of the IABC algorithm. The results show that the identified hysteresis model agreed well with the actual actuator response. Moreover, the identification results were compared with the standard particle swarm optimization (PSO) method, and it can be seen that the search performance in convergence rate of the IABC algorithm is better than that of the standard PSO method.

scholarly journals MODIFIKASI BARU ALGORITMA KOLONI LEBAH BUATAN UNTUK MASALAH OPTIMASI GLOBAL

2018 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Nursyiva Irsalinda ◽  
Sugiyarto Surono
Keyword(s):  
Food Source ◽  
Artificial Bee Colony ◽  
Selection Process ◽  
Optimization Technique ◽  
Food Sources ◽  
Abc Algorithm ◽  
Average Value ◽  
Bee Colony ◽  
Objective Function Values ◽  
Better Than

Artificial Bee Colony (ABC) algorithm is one of metaheuristic optimization technique based on population. This algorithm mimicking honey bee swarm to find the best food source. ABC algorithm consist of four phases: initialization phase, employed bee phase, onlooker bee phase and scout bee phase. This study modify the onlooker bee phase in selection process to find the neighborhood food source. Not all food sources obtained are randomly sought the neighborhood as in ABC algorithm. Food sources are selected by comparing their objective function values. The food sources that have value lower than average value in that iteration will be chosen by onlooker bee to get the better food source. In this study the modification of this algorithm is called New Modification of Artificial Bee Colony Algorithm (MB-ABC). MB-ABC was applied to 4 Benchmark functions. The results show that MB-ABC algorithm better than ABC algorithm

An Improved Method of Artificial Bee Colony Algorithm

2011 ◽  
Vol 101-102 ◽  
pp. 315-319 ◽  
Author(s):  
Xin Jie Wu ◽  
Duo Hao ◽  
Chao Xu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Simulation Experiment ◽  
Optimization Problems ◽  
Local Extremum ◽  
Global Search ◽  
Improved Method ◽  
Object Function ◽  
Bee Colony ◽  
Better Than

The basic artificial bee colony algorithm gets local extremum easily and converges slowly in optimization problems of the multi-object function. In order to enhance the global search ability of basic artificial bee colony algorithm, an improved method of artificial bee colony algorithm is proposed in this paper. The basic idea of this method is as follows: On the basis of traditional artificial bee colony algorithm, the solution vectors that found by each bee colony are recombined after each iteration, then the solution vectors of combinations are evaluated again, thus the best result is found in this iteration. In this way the possibility of sticking at local extremum is reduced. Finally the simulation experiment has been finished. The simulation experiment results have shown that the method proposed in this paper is feasible and effective, it is better than basic artificial bee colony algorithm in the global search ability.

scholarly journals Using an Improved Artificial Bee Colony Algorithm for Parameter Estimation of a Dynamic Grain Flow Model

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
He Wang ◽  
Hongbin Liang ◽  
Lei Gao
Keyword(s):  
Parameter Estimation ◽  
Artificial Bee Colony ◽  
Flow Model ◽  
Nonlinear Least Squares ◽  
The Other ◽  
Practical Application ◽  
Unknown Parameters ◽  
Application Performance ◽  
Bee Colony ◽  
Grain Flow

An effective method is proposed to estimate the parameters of a dynamic grain flow model (DGFM). To this end, an improved artificial bee colony (IABC) algorithm is used to estimate unknown parameters of DGFM with minimizing a given objective function. A comparative study of the performance of the IABC algorithm and the other ABC variants on several benchmark functions is carried out, and the results present a significant improvement in performance over the other ABC variants. The practical application performance of the IABC is compared to that of the nonlinear least squares (NLS), particle swarm optimization (PSO), and genetic algorithm (GA). The compared results demonstrate that IABC algorithm is more accurate and effective for the parameter estimation of DGFM than the other algorithms.

Artificial Bee Colony-Fuzzy Q Learning for Reinforcement Fuzzy Control (Truck Backer-Upper Control Problem)

2016 ◽  
Vol 24 (01) ◽  
pp. 123-136 ◽  
Author(s):  
Sima Saeed ◽  
Aliakbar Niknafs
Keyword(s):  
Fuzzy Control ◽  
Fuzzy System ◽  
Food Source ◽  
Artificial Bee Colony ◽  
Fuzzy Inference ◽  
Food Resource ◽  
Q Value ◽  
Inference System ◽  
Q Learning ◽  
Bee Colony

A new method for reinforcement fuzzy controllers is presented by this article. The method uses Artificial Bee Colony algorithm based on Q-Value to control reinforcement fuzzy system; the algorithm is called Artificial Bee Colony-Fuzzy Q learning (ABC-FQ). In fuzzy inference system, precondition part of rules is generated by prior knowledge, but ABC-FQ algorithm is responsible to achieve the best combination of actions for the consequence part of the rules. In ABC-FQ algorithm, each combination of actions is considered a food source for consequence part of the rules and the fitness level of this food source is determined by Q-Value. ABC-FQ Algorithm selects the best food resource, which is the best combination of actions for fuzzy system, using Q criterion. This algorithm tries to generate the best reinforcement fuzzy system to control the agent. ABC-FQ algorithm is used to solve the problem of Truck Backer-Upper Control, a reinforcement fuzzy control. The results have indicated that this method arrives to a result with higher speed and fewer trials in comparison to previous methods.

Research of Parallel Artificial Bee Colony Algorithm Based on MPI

2013 ◽  
Vol 380-384 ◽  
pp. 1430-1433 ◽  
Author(s):  
Ying Sen Hong ◽  
Zhen Zhou Ji ◽  
Chun Lei Liu
Keyword(s):  
Parallel Algorithm ◽  
Message Passing ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Complex Function ◽  
Function Optimization ◽  
Search Performance ◽  
Bee Colony ◽  
Long Time

Artificial bee colony algorithm is a smart optimization algorithm based on the bees acquisition model. A long time for the search of the artificial bee colony algorithm, in this paper we propose a parallel algorithm of artificial bee colony algorithm (MPI-ABC), with an application of a parallel programming environment MPI, using the programming mode of message passing rewriting the serial algorithm in parallel. Finally, this paper compare both serial and parallel algorithm with testing on complex function optimization problems. The experimental results show that the algorithm is effective to improve the search performance, especially for high-dimensional complex optimization problem.

scholarly journals Multiple Crack Detection in Thick-walled Pipes Using Artificial Bee Colony Algorithm

2021 ◽  
Author(s):  
Amir Banimahd ◽  
Mohammad Amir Rahemi
Keyword(s):  
Objective Function ◽  
Stiffness Matrix ◽  
Crack Detection ◽  
Artificial Bee Colony ◽  
Dynamic Properties ◽  
Mass Matrix ◽  
Multiple Cracks ◽  
Unknown Parameters ◽  
Abc Algorithm ◽  
Bee Colony

An analytical method for diagnosis of cracks in thick-walled pipes with a circular hollow section is investigated in this study. In the proposed method, the defect is assumed to be a non-leaking crack, which is modeled by a massless linear spring with infinitesimal length at the crack location. In order to find the cracks in the pipe, the vibration-based method related to the modal properties of the pipe is utilized. In the modal analysis, the mass and stiffness matrices influence the dynamic properties of the pipe. It is assumed that the mass matrix remains unchanged after the crack initiation, while the corresponding stiffness matrix changes. The stiffness matrix of a cracked element can be formulated by the finite element method with two unknown parameters: location and depth of the crack. Using the eigensolution for an undamped dynamic system to formulate the objective function yields to a complicated optimization problem, which can be solved by an iterative numerical optimization method. Among the optimization approaches, the Artificial Bee Colony (ABC) algorithm is a simple and flexible technique for minimizing the objective function. In this paper, the analytical model is utilized to find the size and position of cracks in a pipe using the ABC algorithm and subsequently some numerical examples are examined in order to assess the accuracy of the method. The results show that the proposed method is able to acceptably estimate the location and depth of multiple cracks in the straight pipes as well as curved ones.

Inferring the atmospheric duct from radar sea clutter using the improved artificial bee colony algorithm

2018 ◽  
Vol 10 (4) ◽  
pp. 437-445 ◽  
Author(s):  
Chao Yang ◽  
Lixin Guo
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Orthogonal Experimental Design ◽  
Sea Clutter ◽  
Particle Swarm Optimizer ◽  
Bee Colony ◽  
Opposition Based Learning ◽  
Atmospheric Duct ◽  
Comparison Results ◽  
Better Than

AbstractIn this paper, an orthogonal crossover artificial bee colony (OCABC) algorithm based on orthogonal experimental design is presented and applied to infer the marine atmospheric duct using the refractivity from clutter technique, and the radar sea clutter power is simulated by the commonly used parabolic equation method. In order to test the accuracy of the OCABC algorithm, the measured data and the simulated clutter power with different noise levels are, respectively, utilized to estimate the evaporation duct and surface duct. The estimation results obtained by the proposed algorithm are also compared with those of the comprehensive learning particle swarm optimizer and the artificial bee colony algorithm combined with opposition-based learning and global best search equation. The comparison results demonstrate that the performance of proposed algorithm is better than those of the compared algorithms for the marine atmospheric duct estimation.

scholarly journals A Tristage Adaptive Biased Learning for Artificial Bee Colony

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Qiaoyong Jiang ◽  
Yueqi Ma ◽  
Yanyan Lin ◽  
Jianan Cui ◽  
Xinjia Liu ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Learning Strategy ◽  
Population Distribution ◽  
Food Sources ◽  
Search Direction ◽  
Bee Colony ◽  
Benchmark Suite ◽  
Over Exploitation ◽  
Better Than ◽  
Biased Information

In recent ten years, artificial bee colony (ABC) has attracted more and more attention, and many state-of-the-art ABC variants (ABCs) have been developed by introducing different biased information to the search equations. However, the same biased information is employed in employed bee and onlooker bee phases, which will cause over exploitation and lead to premature convergence. To overcome this limit, an effective framework with tristage adaptive biased learning is proposed for existing ABCs (TABL + ABCs). In TABL + ABCs, the search direction in the employed bee stage is guided by learning the ranking biased information of the parent food sources, while in the onlooker bee stage, the search direction is determined by extracting the biased information of population distribution. Moreover, a deletion-restart learning strategy is designed in scout bee stage to prevent the potential risk of population stagnation. Systematic experiment results conducted on CEC2014 competition benchmark suite show that proposed TABL + ABCs perform better than recently published AEL + ABCs and ACoS + ABCs.

scholarly journals An improved artificial bee colony algorithm with elite-guided search equations

2017 ◽  
Vol 14 (3) ◽  
pp. 751-767 ◽  
Author(s):  
Zhenxin Du ◽  
Dezhi Han ◽  
Guangzhong Liu ◽  
Jianxin Jia
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
State Of The Art ◽  
Search Process ◽  
Success Rates ◽  
Solution Quality ◽  
Guided Search ◽  
Bee Colony ◽  
Better Than ◽  
Large Disturbance

ABC_elite, a novel artificial bee colony algorithm with elite-guided search equations, has been put forward recently, with relatively good performance compared with other variants of artificial bee colony (ABC) and some non-ABC methods. However, there still exist some drawbacks in ABC_elite. Firstly, the elite solutions employ the same equation as ordinary solutions in the employed bee phase, which may easily result in low success rates for the elite solutions because of relatively large disturbance amplitudes. Secondly, the exploitation ability of ABC_elite is still insufficient, especially in the latter half of the search process. To further improve the performance of ABC_elite, two novel search equations have been proposed in this paper, the first of which is used in the employed bee phase for elite solutions to exploit valuable information of the current best solution, while the second is used in the onlooker bee phase to enhance the exploitation ability of ABC_elite. In addition, in order to better balance exploitation and exploration, a parameter Po is introduced into the onlooker bee phase to decide which search equation is to be used, the existing search equation of ABC_elite or a new search equation proposed in this paper. By combining the two novel search equations together with the new parameter Po, an improved ABC_elite (IABC_elite) algorithm is proposed. Based on experiments concerning 22 benchmark functions, IABC elite has been compared with some other state-of-the-art ABC variants, showing that IABC_elite performs significantly better than ABC_elite on solution quality, robustness, and convergence speed.

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