scholarly journals A Comparative Study of Improved Artificial Bee Colony Algorithms Applied to Multilevel Image Thresholding

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Kanjana Charansiriphaisan ◽  
Sirapat Chiewchanwattana ◽  
Khamron Sunat
Keyword(s):  
High Performance ◽  
Artificial Bee Colony ◽  
Computational Cost ◽  
Multilevel Thresholding ◽  
Image Thresholding ◽  
Bee Colony ◽  
Function Calls ◽  
Optimal Thresholds ◽  
Otsu’S Method ◽  
High Computational Cost

Multilevel thresholding is a highly useful tool for the application of image segmentation. Otsu’s method, a common exhaustive search for finding optimal thresholds, involves a high computational cost. There has been a lot of recent research into various meta-heuristic searches in the area of optimization research. This paper analyses and discusses using a family of artificial bee colony algorithms, namely, the standard ABC, ABC/best/1, ABC/best/2, IABC/best/1, IABC/rand/1, and CABC, and some particle swarm optimization-based algorithms for searching multilevel thresholding. The strategy for an onlooker bee to select an employee bee was modified to serve our purposes. The metric measures, which are used to compare the algorithms, are the maximum number of function calls, successful rate, and successful performance. The ranking was performed by Friedman ranks. The experimental results showed that IABC/best/1 outperformed the other techniques when all of them were applied to multilevel image thresholding. Furthermore, the experiments confirmed that IABC/best/1 is a simple, general, and high performance algorithm.

scholarly journals Modeling Design Iteration in Product Design and Development and Its Solution by a Novel Artificial Bee Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Tinggui Chen ◽  
Renbin Xiao
Keyword(s):  
Product Design ◽  
Iteration Method ◽  
High Performance ◽  
Artificial Bee Colony ◽  
Market Competition ◽  
Processing System ◽  
Design And Development ◽  
Bee Colony ◽  
Design Iteration ◽  
Inner Iteration

Due to fierce market competition, how to improve product quality and reduce development cost determines the core competitiveness of enterprises. However, design iteration generally causes increases of product cost and delays of development time as well, so how to identify and model couplings among tasks in product design and development has become an important issue for enterprises to settle. In this paper, the shortcomings existing in WTM model are discussed and tearing approach as well as inner iteration method is used to complement the classic WTM model. In addition, the ABC algorithm is also introduced to find out the optimal decoupling schemes. In this paper, firstly, tearing approach and inner iteration method are analyzed for solving coupled sets. Secondly, a hybrid iteration model combining these two technologies is set up. Thirdly, a high-performance swarm intelligence algorithm, artificial bee colony, is adopted to realize problem-solving. Finally, an engineering design of a chemical processing system is given in order to verify its reasonability and effectiveness.

scholarly journals A Pseudo-Label Guided Artificial Bee Colony Algorithm for Hyperspectral Band Selection

2020 ◽  
Vol 12 (20) ◽  
pp. 3456
Author(s):  
Chunlin He ◽  
Yong Zhang ◽  
Dunwei Gong
Keyword(s):  
Classification Accuracy ◽  
Artificial Bee Colony ◽  
Computational Cost ◽  
Band Selection ◽  
Selection Algorithm ◽  
Evolutionary Clustering ◽  
Bee Colony ◽  
Hyperspectral Remote Sensing Images ◽  
Selection Algorithms ◽  
Grid Division

Hyperspectral remote sensing images have characteristics such as high dimensionality and high redundancy. This paper proposes a pseudo-label guided artificial bee colony band selection algorithm with hypergraph clustering (HC-ABC) to remove redundant and noise bands. Firstly, replacing traditional pixel points by super-pixel centers, a hypergraph evolutionary clustering method with low computational cost is developed to generate high-quality pseudo-labels; Then, on the basis of these pseudo-labels, taking classification accuracy as the optimized objective, a supervised band selection algorithm based on artificial bee colony is proposed. Moreover, a noise filtering mechanism based on grid division is designed to ensure the accuracy of pseudo-labels. Finally, the proposed algorithm is applied in 3 real datasets and compared with 6 classical band selection algorithms. Experimental results show that the proposed algorithm can obtain a band subset with high classification accuracy for all the three classifiers, KNN, Random Forest, and SVM.

scholarly journals Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Linguo Li ◽  
Lijuan Sun ◽  
Jian Guo ◽  
Jin Qi ◽  
Bin Xu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Optimal Solution ◽  
Weight Coefficient ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Original Algorithm ◽  
Image Thresholding ◽  
Bee Colony ◽  
Optimal Thresholds ◽  
Objective Function Values

The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur’s entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability.

scholarly journals WearGP: A UQ/ML Wear Prediction Framework for Slurry Pump Impellers and Casings

2020 ◽  
Author(s):  
Anh Tran ◽  
Yan Wang ◽  
John Furlan ◽  
Krishnan V. Pagalthivarthi ◽  
Mohamed Garman ◽  
...  
Keyword(s):  
High Performance ◽  
Computational Cost ◽  
Operating Conditions ◽  
Wear Prediction ◽  
Wear Rates ◽  
Computing Platform ◽  
Multi Phase ◽  
Performance Computing ◽  
Low Computational Cost ◽  
High Computational Cost

Abstract Dedicated to the memory of John Furlan. Wear prediction is important in designing reliable machinery for slurry industry. It usually relies on multi-phase computational fluid dynamics, which is accurate but computationally expensive. Each run of the simulations can take hours or days even on a high-performance computing platform. The high computational cost prohibits a large number of simulations in the process of design optimization. In contrast to physics-based simulations, data-driven approaches such as machine learning are capable of providing accurate wear predictions at a small fraction of computational costs, if the models are trained properly. In this paper, a recently developed WearGP framework [1] is extended to predict the global wear quantities of interest by constructing Gaussian process surrogates. The effects of different operating conditions are investigated. The advantages of the WearGP framework are demonstrated by its high accuracy and low computational cost in predicting wear rates.

scholarly journals A Quick Artificial Bee Colony Algorithm for Image Thresholding

Information ◽  
2017 ◽  
Vol 8 (1) ◽  
pp. 16 ◽  
Author(s):  
Linguo Li ◽  
Lijuan Sun ◽  
Jian Guo ◽  
Chong Han ◽  
Jian Zhou ◽  
...  
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Image Thresholding ◽  
Bee Colony

scholarly journals Artificial bee colony algorithm applied to optimal power flow solution incorporating stochastic wind power

2021 ◽  
Vol 12 (3) ◽  
pp. 1890
Author(s):  
Vian H. Ahgajan ◽  
Yasir G. Rashid ◽  
Firas Mohammed Tuaimah
Keyword(s):  
Wind Power ◽  
High Performance ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Artificial Bee Colony ◽  
Wind Farms ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Optimal Power ◽  
The Impact

<span lang="EN-US">This paper focuses on the artificial bee colony (ABC) algorithm, which is a nonlinear optimization problem. is proposed to find the optimal power flow (OPF). To solve this problem, we will apply the ABC algorithm to a power system incorporating wind power. The proposed approach is applied on a standard IEEE-30 system with wind farms located on different buses and with different penetration levels to show the impact of wind farms on the system in order to obtain the optimal settings of control variables of the OPF problem. Based on technical results obtained, the ABC algorithm is shown to achieve a lower cost and losses than the other methods applied, while incorporating wind power into the system, high performance would be gained.</span>

Sign in / Sign up

Export Citation Format

Share Document