نتایج جستجو برای: artificial bee colony

تعداد نتایج: 351883  

2012
R. Murugan M. R. Mohan

A Modified Artificial Bee Colony (ABC) algorithm for Economic Dispatch (ED) problem has been proposed. The Artificial Bee Colony (ABC) algorithm which is inspired by the foraging behavior of honey bee swarm gives a solution procedure for solving economic dispatch problem. It provides solution more effective than Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimizati...

2013
Vahid Chahkandi Mahdi Yaghoobi Gelareh Veisi

Feature selection plays an important role in data mining and pattern recognition, especially in the case of large scale data. Feature selection is done due to large amount of noise and irrelevant features in the original data set. Hence, the efficiency of learning algorithms will increase incredibly if these irrelevant data are removed by this procedure. A novel approach for feature selection i...

Journal: :CoRR 2012
Sudarshan Nandy Partha Pratim Sarkar Achintya Das

Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feed forward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-free solution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristic algorithm, mimicking the foraging or food source searching behaviour of bees in a bee...

2012
Yongchang Chen Weiyu Yu Jiuchao Feng

A robust image watermarking scheme based on singular value decomposition (SVD) and discrete wavelet transform (DWT) with Artificial Bee Colony Algorithm is proposed in this paper. Previous SVD based watermarking algorithms have a major drawback of false positive detection. For solving this problem, the similarity measure of U matrix for ownership is checked. To achieve the highest possible robu...

2010
Ivona BRAJEVIC Milan TUBA Milos SUBOTIC

In this paper an improved version of the Artificial Bee Colony (ABC) algorithm adjusted for constrained optimization problems is presented. It has been implemented and tested on several engineering benchmarks which contain discrete and continuous variables. Our results were compared to the results obtained by Simple Constrained Particle Swarm optimization algorithm (SiC-PSO) which showed a very...

2017
L. Sun X. Liang Q. Wang H. Chen

In the process of material composition detection, image analysis is an inevitable problem. Multilevel thresholding based on the OTSU method is one of the most popular image segmentation techniques. The increase of the number of thresholds increases with the exponential increase in computing time. In order to overcome this problem, this paper proposes an artificial bee colony algorithm with a tw...

2012
Marcus Karnan

The aim of this research is the development of a reliable tool to detect early signs of breast cancer in mammographic images. Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death of female worldwide. Mammogram is one of the most excellent technologies currently being used for diagnosing breast cancer. In this paper, the Enhanced Artificial Bee Colony Optim...

2012
Zhen Wang Sanyang Liu Xiangyu Kong

In this paper, a cardinality constrained mean-variance model is introduced for the portfolio optimization problems. This model is a mixed quadratic and integer programming problem for which efficient algorithms do not exist. The use of heuristic algorithms in this case is necessary. Some studies have investigated the cardinality constrained mean-variance model using heuristic algorithm. But alm...

2016
Wu Chunming

In order to improve the convergence and diversity of multiobjective optimization algorithms, the harmonic average distance is employed to improve the aggregating function combined L-rank value. Selection model and searching scheme of artificial bee colony algorithm and diversity maintaining scheme are improved in this paper. This novel many objectives optimization method based on improved artif...

2016
An Gong Yun Gao Xingmin Ma Wenjuan Gong Huayu Li Zhen Gao

K-means algorithm is sensitive to initial cluster centers and its solutions are apt to be trapped in local optimums. In order to solve these problems, we propose an optimized artificial bee colony algorithm for clustering. The proposed method first obtains optimized sources by improving the selection of the initial clustering centers; then, uses a novel dynamic local optimization strategy utili...

نمودار تعداد نتایج جستجو در هر سال

با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید