This paper presents an improved gravitational search algorithm （IGSA） as a hybridization of a relatively recent evolutionary algorithm called gravitational search algorithm （GSA）, with the free search differential...This paper presents an improved gravitational search algorithm （IGSA） as a hybridization of a relatively recent evolutionary algorithm called gravitational search algorithm （GSA）, with the free search differential evolution （FSDE）. This combination incorporates FSDE into the optimization process of GSA with an attempt to avoid the premature convergence in GSA. This strategy makes full use of the exploration ability of GSA and the exploitation ability of FSDE. IGSA is tested on a suite of benchmark functions. The experimental results demonstrate the good performance of IGSA.展开更多
This paper proposes a novel intelligent estimation algorithm in Wireless Sensor Network nodes location based on Free Search,which converts parameter estimation to on-line optimization of nonlinear function and estimat...This paper proposes a novel intelligent estimation algorithm in Wireless Sensor Network nodes location based on Free Search,which converts parameter estimation to on-line optimization of nonlinear function and estimates the coordinates of senor nodes using the Free Search optimization.Compared to the least-squares estimation algorithms,the localization accuracy has been increased significantly,which has been verified by the simulation results.展开更多
In this paper,an improved optimization approach,free search with double populations(FSDP)which is based on free search(FS)algorithm,is proposed.Comparing to FS algorithm,FSDP preserves the sub-optimal solutions and ad...In this paper,an improved optimization approach,free search with double populations(FSDP)which is based on free search(FS)algorithm,is proposed.Comparing to FS algorithm,FSDP preserves the sub-optimal solutions and adopts elitist strategy in the searching process,which effectively avoids falling into local optimum and improves the convergence speed and the search accuracy.Simulation results show that FSDP has a better comprehensive performance over FS,PSO and GA.展开更多
Time-series discord is widely used in data mining applications to characterize anomalous subsequences in time series. Compared to some other discord search algorithms, the direct search algorithm based on the recurren...Time-series discord is widely used in data mining applications to characterize anomalous subsequences in time series. Compared to some other discord search algorithms, the direct search algorithm based on the recurrence plot shows the advantage of being fast and parameter free. The direct search algorithm, however, relies on quasi-periodicity in input time series, an assumption that limits the algorithm＇s applicability. In this paper, we eliminate the periodicity assumption from the direct search algorithm by proposing a reference function for subsequences and a new sampling strategy based on the reference function. These measures result in a new algorithm with improved efficiency and robustness, as evidenced by our empirical evaluation.展开更多
Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. Th...Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. This paper describes a novel method of fast face detection with multi-scale window search free from image resizing. We adopt statistics of gradient images (SGI) as image features and append an overlapping cell array to improve detection accuracy. The SGI feature is scale invariant and insensitive to small difference of pixel value. These characteristics enable the multi-scale window search without image resizing. Experimental results show that processing speed of our method is 3.66 times faster than a conventional method, adopting HOG features combined to an SVM classifier, without accuracy degradation.展开更多
Pattern search algorithms is one of most frequently used methods which were designed to solve the derivative-free optimization problems. Such methods get growing need with the development of science, engineering, econ...Pattern search algorithms is one of most frequently used methods which were designed to solve the derivative-free optimization problems. Such methods get growing need with the development of science, engineering, economy and so on. Inspired by the idea of Hooke and Jeeves, we introduced an integer m in the algorithm which controls the number of steps of iteration update. We mean along the descent direction to allow the algorithm to?go ahead m steps at most to explore whether we can get better solution further. The experiment proved the strategy’s efficiency.展开更多
We discuss a filter-based pattern search method for unconstrained optimization in this paper. For the purpose to broaden the search range we use both filter technique and frames, which are fragments of grids, to provi...We discuss a filter-based pattern search method for unconstrained optimization in this paper. For the purpose to broaden the search range we use both filter technique and frames, which are fragments of grids, to provide a new criterion of iterate acceptance. The convergence can be ensured under some conditions. The numerical result shows that this method is practical and efficient.展开更多
基金supported by the National Natural Science Foundation of China (70871081)the Shanghai Leading Academic Discipline Project of China (S1205YLXK)
文摘This paper presents an improved gravitational search algorithm （IGSA） as a hybridization of a relatively recent evolutionary algorithm called gravitational search algorithm （GSA）, with the free search differential evolution （FSDE）. This combination incorporates FSDE into the optimization process of GSA with an attempt to avoid the premature convergence in GSA. This strategy makes full use of the exploration ability of GSA and the exploitation ability of FSDE. IGSA is tested on a suite of benchmark functions. The experimental results demonstrate the good performance of IGSA.
基金National Research Foundation for the Doctoral Program of Higher Education of China(No.20060266006)the High-school Natural Science Research Foundation of Jiangsu Province(No.07KJB510095)
文摘This paper proposes a novel intelligent estimation algorithm in Wireless Sensor Network nodes location based on Free Search,which converts parameter estimation to on-line optimization of nonlinear function and estimates the coordinates of senor nodes using the Free Search optimization.Compared to the least-squares estimation algorithms,the localization accuracy has been increased significantly,which has been verified by the simulation results.
基金supported in part by the Natural Science Foundation of China under Grants of 61174094,61273138the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant 20090031110029the Tianjin Nature Science Foundation under Grant 10JCZDJC15900.
文摘In this paper,an improved optimization approach,free search with double populations(FSDP)which is based on free search(FS)algorithm,is proposed.Comparing to FS algorithm,FSDP preserves the sub-optimal solutions and adopts elitist strategy in the searching process,which effectively avoids falling into local optimum and improves the convergence speed and the search accuracy.Simulation results show that FSDP has a better comprehensive performance over FS,PSO and GA.
基金Support by Australian Research Council Linkage Grant No. LP 0776417
文摘Time-series discord is widely used in data mining applications to characterize anomalous subsequences in time series. Compared to some other discord search algorithms, the direct search algorithm based on the recurrence plot shows the advantage of being fast and parameter free. The direct search algorithm, however, relies on quasi-periodicity in input time series, an assumption that limits the algorithm＇s applicability. In this paper, we eliminate the periodicity assumption from the direct search algorithm by proposing a reference function for subsequences and a new sampling strategy based on the reference function. These measures result in a new algorithm with improved efficiency and robustness, as evidenced by our empirical evaluation.
文摘Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. This paper describes a novel method of fast face detection with multi-scale window search free from image resizing. We adopt statistics of gradient images (SGI) as image features and append an overlapping cell array to improve detection accuracy. The SGI feature is scale invariant and insensitive to small difference of pixel value. These characteristics enable the multi-scale window search without image resizing. Experimental results show that processing speed of our method is 3.66 times faster than a conventional method, adopting HOG features combined to an SVM classifier, without accuracy degradation.
文摘Pattern search algorithms is one of most frequently used methods which were designed to solve the derivative-free optimization problems. Such methods get growing need with the development of science, engineering, economy and so on. Inspired by the idea of Hooke and Jeeves, we introduced an integer m in the algorithm which controls the number of steps of iteration update. We mean along the descent direction to allow the algorithm to?go ahead m steps at most to explore whether we can get better solution further. The experiment proved the strategy’s efficiency.
文摘We discuss a filter-based pattern search method for unconstrained optimization in this paper. For the purpose to broaden the search range we use both filter technique and frames, which are fragments of grids, to provide a new criterion of iterate acceptance. The convergence can be ensured under some conditions. The numerical result shows that this method is practical and efficient.
