In recently years, due to its power global search ability, artificial bee colony (ABC) has been successfully applied in many real-world problems. But its shortcoming of slow convergence speed still constraints the further applications. In this paper, for further enhancing its merits and conquering this shortcoming, we propose three improved strategies into ABC algorithm. First, we introduce a new array to preserve some elites of population ever achieved. Based on this, the new updating equations are proposed in our paper. Finally, a new updating mechanism for scout bee is proposed to learn from the defined array for further accelerating the convergence rate of population. Compared with the compared modern evolutionary algorithms, experimental results verify our proposed algorithm achieve better performance especially on the accuracy and stability of solutions.
As we known, the life of patients with Parkinson's disease (PD) which cannot be cured fundamentally has changed thoroughly. Then, automatic identification of early Parkinson's disease on feature data sets attracts many medical researchers. At present, machine learning especially deep learning algorithms have been widely adopted in the task of classification and regression, etc. But labeled data sets are rare and expensive to label in many areas, i.e., aerospace, medical. Transfer learning is often employed to solve the problems with small training dataset. In this paper, we proposed a parameters-based transfer learning algorithm to enhance generalization ability and avoid overfitting of the network. Then a new method is utilized to accelerate the training speed of the network, which help the algorithm to achieve results with high speed. At last, the Earth Mover’s Distance (EMD) is introduced into our proposed transfer learning algorithm for enhancing the precise of measurement which represents as a distance metric between the two probability distribution of images. The experimental results compared with other modern algorithms on the common Parkinson’s datasets show the effectiveness of our algorithm.
With the development of computer vision and deep learning, the convolutional neural network has been widely used in image processing such as object detection and semantic segmentation, and has achieved breakthrough achievements. However, when the training samples are insufficient, the conventional neural network usually has unsatisfactory robustness. In order to solve the problem, we improve the generalization performance of the few-shot detectors by focusing on the target center and can identify novel categories. The paper proposes a new attention mechanism based on the auxiliary circle feature map of the object center. By selecting an auxiliary circle feature map with the object center as the center of the circle and the minimum size in height and width as the diameter, adding it to the anchor-free CenterNet network as soft attention to promote network training. Several experiments on PASCAL VOC2007/2012 datasets show that the proposed method achieves the most advanced level in terms of the accuracy and standard deviation of few-shot object detection, which indicates the algorithm’s effectiveness.
The artificial bee colony (ABC) algorithm shows a relatively powerful exploration search capability but show convergence rate, especially on unimodal functions. In this paper, an improved artificial bee colony algorithm is introduced to shorten its computation time. In the proposed algorithm, two novel update equations, utilizing the social experience of the whole population, are proposed to boost the performance of employed bees and onlooker bees respectively. The effectiveness of our algorithm is validated through the basic benchmark functions. Furthermore, a model of feed-forward artificial neural network is also employed to verify the effectiveness of our algorithm. The experimental results show the IUABC algorithm achieves better performance than the other compared algorithms.
Parkinson's disease (PD) is a central nervous system disease that common occurs among older peoples. Automatic identification the degree of early Parkinson's disease is a meaningful work today. In this paper, the diagnostic experiment on Parkinson is executed on feature data sets which contain a variety data of handwriting and speech gathered from Parkinson's patients and ordinary people. Since most data in these datasets contains noise, right selection of feature from subsets plays a significant role on enhancing the performance of classification. To increase the accuracy of classification, this paper proposes an improved discrete artificial bee colony algorithm to determine the optimal subset of features. Our algorithm combines both advantages of filters and wrappers feature selection strategies to eliminate most of the uncorrelated or noisy features which could improve the efficiency of feature selection. The experimental results demonstrate our algorithm achieves better results on both precision and shortening the amounts of subsets among the comparison with other modern Evolutionary Computation algorithms.
Among all the existing segmentation techniques, the thresholding technique is one of the most popular due to its
simplicity, robustness, and accuracy (e.g. the maximum entropy method, Otsu’s method, and K-means clustering).
However, the computation time of these algorithms grows exponentially with the number of thresholds due to their
exhaustive searching strategy. As a population-based optimization algorithm, differential algorithm (DE) uses a
population of potential solutions and decision-making processes. It has shown considerable success in solving complex
optimization problems within a reasonable time limit. Thus, applying this method into segmentation algorithm should be
a good choice during to its fast computational ability. In this paper, we first propose a new differential algorithm with a
balance strategy, which seeks a balance between the exploration of new regions and the exploitation of the already
sampled regions. Then, we apply the new DE into the traditional Otsu’s method to shorten the computation time.
Experimental results of the new algorithm on a variety of images show that, compared with the EA-based thresholding
methods, the proposed DE algorithm gets more effective and efficient results. It also shortens the computation time of
the traditional Otsu method.
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