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About 9,733 papers for “fast search algorithm”

An Algorithm For Fast Median Search

Juan And Vidal, A. Juan, E. Vidal · 1997

Searching for a median of a set of patterns is a well-known technique to model the set or to aid searching for more accurate models such as a generalized median or a k-median. While medians and generalized medians are (relatively) easy to compute in the case of Euclidean representation spaces, this is unfortunately no longer true when more complex distance measures are to be used, which often happens in practical situations. In these cases, a direct method to perform median search is not feasible for large sets of patterns. In order to cope with this computational problem, an algorithm for median search is proposed which is faster than the direct method in terms of distance computations.

Cited by 0

A Fast Discrete Gravitational Search Algorithm

Hasrul Che Shamsudin, Addie Irawan, Zuwairie Ibrahim et al. · 2012

This study introduces a variant of Gravitational Search Algorithm (GSA) for discrete optimization problems, namely, Fast Discrete Gravitational Search Algorithm (FDGSA). The main difference between the proposed FDGSA and the existing Binary Gravitational Search Algorithm (BGSA) is that an agent's position is updated based on its direction and velocity. Both the direction and velocity determine the candidates of integer values for the position update of an agent and then the selection is done randomly. Unimodal test functions, such as De Jong's function, Scwefel's function and Rosenbrock's valley are used to evaluate the performance of the proposed FDGSA. Comparison with BGSA is done to benchmark the proposed FDGSA in terms of speed of convergence and quality of solution. The experimental result shows that the proposed FDGSA converges faster compared to the BGSA.

Cited by 25

A fast discrete gravitational search algorithm

Hasrul, Che Shamsudin, Irawan, Addie, Zuwairie, Ibrahim et al. · UMP Institutional Repository (Universiti Malaysia Pahang) · 2012

This study introduces a variant of Gravitational Search Algorithm (GSA) for discrete optimization problems, namely, Fast Discrete Gravitational Search Algorithm (FDGSA). The main difference between the proposed FDGSA and the existing Binary Gravitational Search Algorithm (BGSA) is that an agent's position is updated based on its direction and velocity. Both the direction and velocity determine the candidates of integer values for the position update of an agent and then the selection is done randomly. Unimodal test functions, such as De Jong's function, Scwefel's function and Rosenbrock's valley are used to evaluate the performance of the proposed FDGSA. Comparison with BGSA is done to benchmark the proposed FDGSA in terms of speed of convergence and quality of solution. The experimental result shows that the proposed FDGSA converges faster compared to the BGSA.

Cited by 0

Fast stochastic and recursive search algorithm

M. Rosvall, C. T. Bergstrom · 2009

Any greedy (fast but inaccurate) or Monte Carlo-based (accurate but slow) approach can be used to minimize the map equation. But since on the order of 1000 networks must be clustered for each significance clustering and high accuracy always is desirable, we have developed a new method that provides a good balance between the two extremes. As a reference, the new algorithm is in practice as fast as the previous high-speed algorithms (the greedy search presented in the supporting appendix of ref. (1)), which were based on the method introduced in ref. (2) and refined in ref. (3). Yet it is also more accurate than our previous high-accuracy algorithm (a simulated annealing approach) presented in the same supporting appendix. The core of the algorithm follows closely the method

Cited by 0

An Algorithm for Fast Median Search

A. Juan, E. Vidal · 1997

Searching for a median of a set of patterns is a well-known technique to model the set or to aid searching for more accurate models such as a generalized median or a k-median. While medians and generalized medians are (relatively) easy to compute in the case of Euclidean representation spaces, this is unfortunately no longer true when more complex distance measures are to be used, which often happens in practical situations. In these cases, a direct method to perform median search is not feasible for large sets of patterns. In order to cope with this computational problem, an algorithm for median search is proposed which is faster than the direct method in terms of distance computations.

Cited by 0

An Improved Fast Search Algorithm Based on A* Algorithm

Xuan Wang, Fan Yang · 2025

A* algorithm is a path planning algorithm widely used in various traffic carriers. However, in complex environment, it needs to plan the path in real time, which puts higher requirements on the efficiency of the algorithm. This paper presents an improved A* algorithm, which can greatly improve the planning efficiency of A* algorithm without increasing the path step size. First, allow and give priority to search the diagonal direction of the region, then improve the heuristic function, combine the linear distance and diagonal distance, make the cost value close to the real distance, and use the dynamic weight to adjust. Under the same conditions, the traditional A* algorithm and the improved A* algorithm are simulated and compared by Matlab, and the real scene is simulated and tested to verify the effectiveness of the improved A* algorithm. Simulation results show that compared with the traditional A* algorithm, the improved algorithm can not only find the optimal global path, but also has higher search efficiency, which is conducive to improving the efficiency of path planning, and can meet the requirements of real-time path planning in complex scenarios.

Cited by 0Open access

A Fast Partial Distance Search Algorithm for

Vq Codebook, Shinfeng D. Lin, Shih-chieh Shie et al. · 2008

Vector quantization (VQ) is an efficient technique for image compression. However, one of the most serious problems of VQ is the heavily computational load for searching the closest codeword to the input vector in the codebook. To overcome this problem, we propose a simple and fast partial distance search algorithm for VQ codebook by using the sum-norm pyramid structure of codewords. The proposed scheme can eliminate many codewords that are impossible to be closest to the input vector in the codebook. Therefore, it can save a great deal of computational time for the image encoding process of VQ. Experimental results show that the proposed scheme significantly reduces the encoding time of VQ and obtains approximately the same image quality as the full search algorithm (FSA). It also shows that our scheme outperforms the recent fast codeword search algorithms in terms of the coding time.

Cited by 0

UMHexagonS search algorithm for fast motion estimation

Lifen Xie, Chunqing Huang, Bihui Chen · 2011

In video coding, motion estimation is the most time consuming part due to its high computational complexity. Based on the high temporal and spatial correlation of motion vector (MV), a new fast motion estimation algorithm of UMHexagonS (UMH) has been proposed to reduce computational complexity by using relatively few search points without degrading image quality, in which the modified patterns with new uneven cross, multi-hexagon-grid and hexagon are applied to. The proposed algorithm alleviates the computational burden and maintains the quality of video. Compared with the original UMH algorithm, the proposed algorithm has a better performance, and it reduces the number of search points by 32% at least and preserves similar average peak signal-to-noise ratio (PSNR) value at the same time.

Cited by 11

An Improved Mean Pyramid Fast Search Algorithm

Yanjie Wang · 2007

Aimed at solving the problem which consisting in conventional mean pyramid structure search algorithm that we are not sure from which point to start and we don't have restrictions to narrow the search range,this paper presents an improved fast search algorithm,which can start the search from the point that near the closest codeword and narrow the search range.This algorithm makes use of the indwelling inequation in pyramid structure and arranges codewords according to bintree structure.We avoid many useless search at the expense of additional storage space.Experiment result shows that the proposed algorithm has a higher efficiency over conventional mean pyramid search algorithm at the same accuracy level.

Cited by 1

Fast motion estimation algorithm combining search point sampling technique with adaptive search range algorithm

Yun-Ho Ko, Hyun‐Soo Kang, Jae‐Won Suh · 2012

This paper presents an enhanced fast motion estimation method where a search point sampling technique is combined with the adaptive search range algorithm (ASRA) based on the distribution of motion vector differences, which is our previous work. Since the ASRA is based on downsizing of search ranges for less computational complexity rather than sub-sampling of search points that is adopted by most of the fast algorithms, it results in smaller search areas where all points are considered as search points. Therefore, the conventional fast algorithms based on search point sampling techniques such as three-step search algorithm can be easily employed to the ASRA. As a result, we propose an algorithm where a part of the points within the search areas determined by the ASRA are sampled as the search points. Experimental results show that the proposed method reduces complexity of our ASRA by about 60% without quality degradation.

Cited by 3

Reduced Hexagonal Search Algorithm for Fast Motion Estimation

Prashant A. Bhalge, Salim Y. Amdani · 2018

Motion Estimation plays a strategic role in a motion compensated video coding scheme. The block matching technique has been accepted by many video compression standards. Motion Estimation is the most computational severe part in the encoder. Due to its elementary influence on coding efficiency and its high necessities in both processing power and data bandwidth, the Motion Estimation algorithms optimization has been comprehensively studied for the last two decades.

Cited by 1

Fast search algorithm for high dimensional pattern analysis

Jiaqi Zhu, Sirajudeen Gulam Razul · 2007

Nearest neighbor search is used to identify which class a query sample belongs to. The most widely used method is the shortest Euclidean distance measure and it is accepted as the simplest and most effective method for pattern analysis. In pattern analysis, we are only interested in finding out the relevant class rather than the relevant training sample, thus existing algorithms are inefficient in that they try to find an exact training sample instead of a class, so it takes a long time to decide, especially when the dimension of a dataset is very high. In this paper we will present an efficient algorithm for directly searching the nearest class instead of the nearest training sample. Experiments show that our algorithm is much more efficient than the standard tree search methodologies.

Cited by 1

Fast Random Search Algorithm in Neural networks Training

Matskevich, Vadim · Digital Library of the Belarusian State University (Belarusian State University) · 2023

The paper deals with a state-of-art applied problem related to the neural networks training. Currently, gradient descent algorithms are widely used for training. Despite their high convergence rate, they have a number of disadvantages, which, with the expansion of the neural networks' scope, can turn out to be critical. The paper proposes a fast algorithm for neural networks training based on random search. It has been experimentally shown that in terms of the proposed algorithm's convergence rate, it is almost comparable to the best of the gradient algorithms, and in terms of quality it is significantly ahead of it

Cited by 0Open access

A new compression algorithm for fast text search

Aydin CARUS, Altan Mesut · DergiPark (Istanbul University) · 2016

We propose a new compression algorithm that compresses plain texts by using a dictionary-based model and a compressed string-matching approach that can be used with the compressed texts produced by this algorithm. The compression algorithm (CAFTS) can reduce the size of the texts to approximately 41% of their original sizes. The presented compressed string matching approach (SoCAFTS), which can be used with any of the known pattern matching algorithms, is compared with a powerful compressed string matching algorithm (ETDC) and a compressed string-matching tool (Lzgrep). Although the search speed of ETDC is very good in short patterns, it can only search for exact words and its compression performance differs from one natural language to another because of its word-based structure. Our experimental results show that SoCAFTS is a good solution when it is necessary to search for long patterns in a compressed document.

Cited by 0

Fast K-nearest-neighbour search algorithm for nonparametricclassification

Seung Joon Baek, Koeng-Mo Sung · Electronics Letters · 2000

A fast KNN search algorithm for nonparametric classification is presented. The proposed algorithm uses a projection vector to accelerate the classification process by eliminating the need to calculate a large number of distances. The algorithm also uses a linked list to efficiently retain the intermediate k closest vector and is combined with the PDS technique to obtain further acceleration. Simulation results confirm the effectiveness of the proposed algorithms.

Cited by 15

Research of Fast Search Algorithm for Object Region

Dong Hui · 2002

This paper uses two kinds of fast search algorithms that are based on template, according to relative matching principles four branch trees and fast template . Using these methods for fast searching object can increase the object searching speed greatly.

Cited by 0

A fast search algorithm for speech vector quantisation

J. Ngwa-Ndifor, Tim Ellis · Research Repository (Kingston University London) · 1992

The authors consider the problem of fast search algorithms used in vector quantisation. Some currently used fast search methods are described and a new implementation based on the coupling of some of these algorithms is proposed and shown to provide better performance than the constituent algorithms on their own. The new algorithm can also be useful in the codebook design stage.

Cited by 0