scholarly journals Kinematics Analysis of 5R axis using Evolutionary Algorithms

2019 ◽  
Vol 8 (3) ◽  
pp. 7028-7033
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Degrees Of Freedom ◽  
Artificial Bee Colony ◽  
Robotic Manipulators ◽  
Absolute Error ◽  
Kinematics Analysis ◽  
Bee Colony ◽  
Revolute Joints ◽  
Minimization Of Errors

In this paper, we have represented the kinematics solution of five degrees of freedom articulated arm with five revolute joints using Evolutionary algorithm. DH parameters are used to obtain the kinematics analysis of the manipulator. Simulations are performed on the MATLAB to show the workspace of the robotic manipulators. Firefly and artificial bee colony algorithms (ABC) have been used for the minimization of errors. The position error and absolute error have been minimized to the acceptable level.

scholarly journals An automated system analysis and design tool for spacecrafts ceas space journal

2021 ◽  
Author(s):  
Manfred Ehresmann ◽  
Georg Herdrich ◽  
Stefanos Fasoulas
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Degrees Of Freedom ◽  
System Analysis ◽  
Propulsion System ◽  
Optimal System ◽  
Automated System ◽  
Processing Unit ◽  
Data Set ◽  
Analysis And Design

AbstractIn this paper, a generic full-system estimation software tool is introduced and applied to a data set of actual flight missions to derive a heuristic for system composition for mass and power ratios of considered sub-systems. The capability of evolutionary algorithms to analyse and effectively design spacecraft (sub-)systems is shown. After deriving top-level estimates for each spacecraft sub-system based on heuristic heritage data, a detailed component-based system analysis follows. Various degrees of freedom exist for a hardware-based sub-system design; these are to be resolved via an evolutionary algorithm to determine an optimal system configuration. A propulsion system implementation for a small satellite test case will serve as a reference example of the implemented algorithm application. The propulsion system includes thruster, power processing unit, tank, propellant and general power supply system masses and power consumptions. Relevant performance parameters such as desired thrust, effective exhaust velocity, utilised propellant, and the propulsion type are considered as degrees of freedom. An evolutionary algorithm is applied to the propulsion system scaling model to demonstrate that such evolutionary algorithms are capable of bypassing complex multidimensional design optimisation problems. An evolutionary algorithm is an algorithm that uses a heuristic to change input parameters and a defined selection criterion (e.g., mass fraction of the system) on an optimisation function to refine solutions successively. With sufficient generations and, thereby, iterations of design points, local optima are determined. Using mitigation methods and a sufficient number of seed points, a global optimal system configurations can be found.

Iterative learning control of robot based on artificial bee colony algorithm

2019 ◽  
Vol 233 (9) ◽  
pp. 1221-1238
Author(s):  
Wanqiang Xi ◽  
Yaoyao Wang ◽  
Bai Chen ◽  
Hongtao Wu
Keyword(s):  
Iterative Learning Control ◽  
Degrees Of Freedom ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Control Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Industrial Robots ◽  
Control Effect ◽  
Bee Colony

For the repetitive motion control, inaccurate model, and other issues of industrial robots, this article presents a novel control method that the proportion differentiation-type iterative learning parameters are self-tuning based on artificial bee colony algorithm. Considering the influence of the numerical value of iterative learning parameters on the control system, especially in the early iteration, the control effect is not satisfactory. Thus, the artificial bee colony algorithm is introduced in this article. Using bee colony as search unit, the parameters in iterative learning are optimized through the exchange of information and the survival of fittest between them. And then the optimized results are returned to iterative learning control algorithm. Finally, the digital simulation of a two-degrees-of-freedom manipulator and the experimental verification of a cable-driven robot with its first two joints are carried out. The results show that the iterative learning control based on the artificial bee colony algorithm has faster convergence and better control effect than the iterative learning control with fixed parameters.

scholarly journals Artificial Bee Colony Optimization Based Despeckling Framework for Ultrasound Images

2020 ◽  
Vol 13 (5) ◽  
pp. 20-32
Author(s):  
Pradeep K. Gupta ◽  
◽  
Shyam Lal ◽  
Farooq Husain
Keyword(s):  
Artificial Bee Colony ◽  
Similarity Index ◽  
Absolute Error ◽  
Thoracic Wall ◽  
Ultrasound Images ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

This paper proposed an artificial bee colony optimization (ABC) algorithm based despeckling framework to overcome the effect of speckle noise present in real ultrasound images. A low pass filter and fast non-local mean filter along with Artificial Bee Colony (ABC) optimization algorithm are used for the quality enhancement of ultrasound images. The output results obtained for the real ultrasound images filtered with the proposed approach and the other most studied approaches discussed in the literature. The outperformance of the proposed method is verified by calculation of peak signal to noise ratio (PSNR), mean square error (MSE), mean absolute error (MAE), and structure similarity index (SSIM) quality measures. The proposed filtering approach is tested on eight real clinical ultrasound images of adrenal gland, appendicitis, bladder, pancreas, parathyroid gland, scrotal gland, thoracic wall, and uterus. The experimental results yield that the quantitative and qualitative results of the proposed framework are better than benchmark despeckling methods compared to real ultrasound images. Further, the proposed framework also preserves the fine details in real ultrasound images.

A HYBRID ARTIFICIAL BEE COLONY OPTIMIZATION AND QUANTUM EVOLUTIONARY ALGORITHM FOR CONTINUOUS OPTIMIZATION PROBLEMS

2010 ◽  
Vol 20 (01) ◽  
pp. 39-50 ◽  
Author(s):  
HAI-BIN DUAN ◽  
CHUN-FANG XU ◽  
ZHI-HUI XING
Keyword(s):  
Evolutionary Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Experimental Comparison ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Comparison Results ◽  
Bee Colony Optimization ◽  
Continuous Optimization Problems

In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems.

scholarly journals Artificial Bee Colony Algorithm Improved with Evolutionary Operators

2018 ◽  
Vol 18 (02) ◽  
pp. e13 ◽  
Author(s):  
Gabriela Minetti ◽  
Carolina Salto
Keyword(s):  
Evolutionary Algorithms ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Systematic Investigation ◽  
Original Method ◽  
Recombination Process ◽  
Test Problems ◽  
Computational Results ◽  
Bee Colony ◽  
Evolutionary Operators

In this paper, we design, implement, and analysis the replacement of the method to create new solutions in artificial bee colony algorithm by recombination operators, since the original method is similar to the recombination process used in evolutionary algorithms. For that purpose, we present a systematic investigation of the effect of using six different recombination operators for real-coded representations at the employed bee step. All the analysis is carried out using well known test problems. The experimental results suggest that the method to generate a new candidate food position plays an important role in the performance of the algorithm. Computational results and comparisons show that three of the six proposed algorithms are very competitive with the traditional bee colony algorithm.

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