This paper studied the problem of parallel processing machine scheduling, taking both set up time and run-based preventive maintenance with reliability constraints into consideration. The objective is to minimize make...
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ISBN:
(纸本)9781538667866
This paper studied the problem of parallel processing machine scheduling, taking both set up time and run-based preventive maintenance with reliability constraints into consideration. The objective is to minimize makespan. For this NP-hard problem, an antcolonyoptimization (ACO) algorithm is proposed. The node selecting probability equation is set based on characteristics of this problem. The objective value obtained by the proposed algorithm is compared to that of the classical LPT rule through numerical experiments. The experiment results imply that the proposed ACO algorithm has better performance than the LPT rule.
To solve the scheduling problem of workflow tasks in cloud computing, this paper combined the improved fuzzy c-means clustering algorithm (IFCM) and the improved ant colony optimization algorithm (IACO) and proposed a...
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ISBN:
(纸本)9783030000097;9783030000080
To solve the scheduling problem of workflow tasks in cloud computing, this paper combined the improved fuzzy c-means clustering algorithm (IFCM) and the improved ant colony optimization algorithm (IACO) and proposed a new workflow task scheduling algorithm. Firstly, the proposed algorithm used the IFCM to classify resources. Then, tasks will be sorted by their priority. Based on the results of resource clustering and the distance between resources and expect of tasks, tasks will be assigned to the appropriate resources and the scheduling will be initialized. After that, the workflow tasks will be encoded based on the initial scheduling. At last, ant colony optimization algorithm will be improved by the cross and mutation operation in genetic algorithm and used to search optimal schedules. The experiments showed that the proposed algorithm could quickly and efficiently find appropriate scheduling scheme, effectively reduce the time span of workflow tasks and increase the utilization of resources.
This paper proposed a novel hybrid algorithm for path planning at macroscopic level for autonomous climbing robot. The path planning is an Asymmetric Traveling Salesman Problem (ATSP). The problem can be decomposed in...
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ISBN:
(纸本)9781538695944
This paper proposed a novel hybrid algorithm for path planning at macroscopic level for autonomous climbing robot. The path planning is an Asymmetric Traveling Salesman Problem (ATSP). The problem can be decomposed into some groups with small-scale points by Density Peaks Clustering algorithm (DPC), and these groups are divided into two types based on the node distribution: dense groups and sparse groups. Then local paths for all groups are solved by ant colony optimization algorithm (ACO) and merged at the nearest pair of points between the two adjacent groups to generate the initial global path. At last, the final global path is obtained after optimizing by K-Opt algorithms. The proposed algorithm is tested on nine benchmark instances compared with four other algorithms. Simulation experiment result shows that the proposed algorithm can provide the solution with higher accuracy and shorter runtime.
In the system of the image defect detection system, during image acquisition and transmission, the salt-and-pepper Noise will adversely affect the subsequent processing and recognition. To eliminate the salt-and-peppe...
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ISBN:
(纸本)9783319733173;9783319733166
In the system of the image defect detection system, during image acquisition and transmission, the salt-and-pepper Noise will adversely affect the subsequent processing and recognition. To eliminate the salt-and-pepper noise effectively, a defect image denoising algorithm based on antcolonyoptimization wavelet threshold is improved in this paper. Firstly, the basic principle of wavelet denoising is analyzed theoretically, and a compromise threshold function and a GCV optimal threshold selection method are adopted. It uses antcolonyalgorithm to optimize the wavelet threshold, which greatly improves the speed and accuracy of the optimal threshold. Using standard soft threshold method, GCV threshold optimization method and the antcolonyoptimization wavelet threshold method, the defect image of the lens is denoised. The results of experiment indicate that the algorithm can remove the salt-and-pepper noise in the image of defective lenses more effectively than the other two algorithms, and improve the accuracy of the lens detection. This algorithm is also suitable for general image denoising.
Protein methylation is involved in dozens of biological processes and plays an important role in adjusting protein physicochemical properties, conformation and function. However, with the rapid increase of protein seq...
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Protein methylation is involved in dozens of biological processes and plays an important role in adjusting protein physicochemical properties, conformation and function. However, with the rapid increase of protein sequence entering into databanks, the gap between the number of known sequence and the number of known methylation annotation is widening rapidly. Therefore, it is vitally significant to develop a computational method for quick and accurate identification of methylation sites. In this study, a novel predictor (Methy_SVMIACO) based on support vector machine (SVM) and improved ant colony optimization algorithm (IACO) is developed to identify methylation sites. The IACO is utilized to find the optimal feature subset and parameter of SVM, while SVM is employed to perform the identification of methylation sites. Comparison of the IACO with conventional ACO shows that the IACO converges quickly toward the global optimal solution and it is more useful tool for feature selection and SVM parameter optimization. The performance of Methy_SVMIACO is evaluated with a sensitivity of 85.71%, a specificity of 86.67%, an accuracy of 86.19% and a Matthew's correlation coefficient (MCC) of 0.7238 for lysine as well as a sensitivity of 89.08%, a specificity of 94.07%, an accuracy of 91.56% and a MCC of 0.8323 for arginine in 10-fold cross-validation test. It is shown through the analysis of the optimal feature subset that some upstream and downstream residues play important role in the methylation of arginine and lysine. Compared with other existing methods, the Methy_SVMIACO provides higher Acc, Sen and Spe, indicating that the current method may serve as a powerful complementary tool to other existing approaches in this area. The Methy_SVMIACO can be acquired freely on request from the authors. (C) 2011 Elsevier B.V. All rights reserved.
In today's world, logistic centers not only play an important roles in sustaining the nation's economy, they also significantly contribute to the economic and social development of the regions in which they ar...
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In today's world, logistic centers not only play an important roles in sustaining the nation's economy, they also significantly contribute to the economic and social development of the regions in which they are located. The layout of the center is crucial in ensuring that such important centers are both efficient and productive. To achieve this, this study focuses on the development of a logistic center layout that is integrated with the ant colony optimization algorithm. To this end, the logistic center area layout was developed by applying the developed algorithm to an actual logistic center planned to be constructed. The efficiency of the suggested algorithm was tested in accordance with the benchmark problems in the literature. In addition, a case study was carried out to illustrate the effectiveness of the proposed approach. The obtained results revealed that the suggested algorithm provided more efficiency than other layouts.
To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in antcolonyoptimization (ACO) algorithm in solving complex optimization problems, the chaotic o...
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To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in antcolonyoptimization (ACO) algorithm in solving complex optimization problems, the chaotic optimization method, multi-population collaborative strategy and adaptive control parameters are introduced into the GA and ACO algorithm to propose a genetic and antcolony adaptive collaborative optimization (MGACACO) algorithm for solving complex optimization problems The proposed MGACACO algorithm makes use of the exploration capability of GA and stochastic capability of ACO algorithm. In the proposed MGACACO algorithm, the multi-population strategy is used to realize the information exchange and cooperation among the various populations. The chaotic optimization method is used to overcome long search time, avoid falling into the local extremum and improve the search accuracy. The adaptive control parameters is used to make relatively uniform pheromone distribution, effectively solve the contradiction between expanding search and finding optimal solution. The collaborative strategy is used to dynamically balance the global ability and local search ability, and improve the convergence speed. Finally, various scale TSP are selected to verify the effectiveness of the proposed MGACACO algorithm. The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.
The actual temperature control for consecutive reaction problem is a complex optimization problem. Genetic algorithms (GA) is a metaheuristic inspired by imitating the processes observed during natural evolution. It h...
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The actual temperature control for consecutive reaction problem is a complex optimization problem. Genetic algorithms (GA) is a metaheuristic inspired by imitating the processes observed during natural evolution. It has a strong global search ability and less computation time, but it exists the premature convergence and poor stability. antcolonyoptimization (ACO) is a metaheuristic inspired by imitating the behavior of real ants. It has the robustness and parallel computation, but it exists the slow convergence speed and stagnation phenomenon. In this paper, a new genetic and antcolony self-adaptive hybrid (NGASAH) algorithm based on the chaotic searching strategy, multi-populations and self-adaptive parameter control strategies is presented. In the proposed NGASAH algorithm, the chaotic searching strategy is used to avoid the optimal solution. The strategy of the multiple populations is used to avoid to converge to a local extreme point of all particles. The strategy of self-adaptive parameter control is used to dynamically balance the local search ability and the global ability, and improve the convergence speed. The actual temperature control of consecutive reaction problem is used to test the validity of the NGASAH algorithm. The experiment results show that the NGASAH algorithm can obtain the global search ability and the faster convergence speed in solving the complex optimization problems.
Medical image segmentation plays a dominant role in medical image analysis and clinical research. As an effective method for image segmentation, pulse-coupled neural networks (PCNN) has its own limitation that the val...
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Medical image segmentation plays a dominant role in medical image analysis and clinical research. As an effective method for image segmentation, pulse-coupled neural networks (PCNN) has its own limitation that the values of the parameters have a strong effect on its performance when it is used to segment the image. This paper proposed a new method for medical image segmentation using the self-adaptive PCNN model. In this method, we combined the searching capabilities of antcolonyoptimization (ACO) algorithm in the solution space with the biological characteristics of PCNN, to find the optimal values of PCNN's parameters for each input image. Moreover, the search process of the ACO algorithm was divided into the local searching and the global searching to accelerate the speed of the ASO's convergence. Based on the above work, a new automatic method for the image segmentation, namely ACO-PCNN, was presented. Lastly, four pairs of different MR medical images, including transaxial, sagittal, coronal sections and noisy medical images, were used to test and validate the performance of the proposed method. The experimental results illustrated that our method was accurate and effective to MRI medical images.
This paper studies container loading optimization problem. This problem is a subset of rectangular boxes loaded into a rectangular container with fixed dimensions such that maximize container's utilization ratio. ...
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ISBN:
(纸本)9781424451821
This paper studies container loading optimization problem. This problem is a subset of rectangular boxes loaded into a rectangular container with fixed dimensions such that maximize container's utilization ratio. A mathematical model is given. Some principles which include space division, space merger, residual subspace omitted and loading rule are presented. A hybrid algorithm which integrate ant colony optimization algorithm with above principles is used to solve the container loading problem. The simulation results show that the model and the algorithm are effective.
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