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About 10,000 papers for “Algorithm”

The analysis and application of Prim algorithm, Kruskal algorithm, Boruvka algorithm

Jiayu Chen · Applied and Computational Engineering · 2023

Minimum spanning tree has many applications in real life. For example, the government needs to build roads between many cities. Therefore, it is necessary to find the plan with the shortest path to save the cost. The problem is essentially generating a minimal spanning tree, and it require a suitable algorithm to find the minimum spanning tree. In this paper, the author analyzes the structure and time complexity of the Prim algorithm, the Kruskal algorithm and the Boruvka algorithm. Through this research, the author finds Prim algorithm is suitable for dense graphs. The Kruskal algorithm can generate the minimum spanning tree in sparse tree. And the Boruvka algorithm is suitable for graphs that have some special characters. Based on the above conclusions, the author gives some suggestions for urban highway network planning.

Cited by 2Open access

A Load Flow Algorithm Based on Gauss Algorithm and Fast Decoupling Algorithm

Zhang Li · 2009

In load flow calculation by traditional Gaussian algorithm, the convergence of the computation frequently becomes slower when the PV nodes are transformed into PQ nodes. For this reason, an approach using fast decoupling method to process PV nodes is proposed. In the proposed approach, traditional Gaussian algorithm is used to process PQ nodes and the PQ part of the network is eliminated by Gaussian elimination method, thus a network consisting of PV nodes and a balance bus is obtained, and then the obtained network is solved by fast decoupling method to implement fast convergence of the algorithm. The features of the proposed approach are as following: constant Jacobian matrix, less memory occupation, reliable convergence and high computing speed.

Cited by 1

Hybrid algorithm based on Levenberg-Marquardt Bayesian Regularization Algorithm and Genetic Algorithm

Feng Song, Hongchun Wang · 2013

In order to overcome the insufficiencies of the convergence of the low speed, a low precision of the forecast and easy convergence to a local minimum point of error function on BP Neural Networks (BPNN), a new hybrid algorithm-LMBRGA, which uses both the Levenberg-Marquardt(LM) Bayesian Regularization Algorithm(LMBRA) and Genetic Algorithm(GA) to optimize BPNN, is proposed. The specific process was as follows. Firstly, the GA optimized the best weights and thresholds as the training initial values of BPNN. Then, the BPNN after initialization was trained by the LMBRA until the network has converged. Finally, the network model, which met the requirements after being examined by the test samples, was applied to predict the resident consumption level of Chengdu. By Simulation Experiments analysis, the LMBRGA hybrid algorithm has faster convergence rate than the LMBRA. From the average relative forecasting error (ARFE)'s comparison of the predictive results, it clearly indicates that the forecast precision of the LMBRGA hybrid algorithm is higher than another five optimization algorithms.

Cited by 5

Hybrid cluster algorithm based on generic algorithm and generic fuzzy clustering algorithm

Miao Li · 2007

In order to dynamically get the number of cluster center C and the most optimal cluster sample,we construct a new algorithm based on generic algorithm and generic Fuzzy C-Means(called HGA-FGCM).This method gives a new object evaluation function,which takes into not only the scatter degree among clusters but also the compactness in the same cluster.Make use of the GA’s global optimal search ability;get the optimal clusters under the given number of cluster center C.

Cited by 1

Hybrid Optimization Algorithm of Differential Evolution Algorithm and Artificial Bee Colony Algorithm

Xiaoyu Song, Yanlin Zhu, Ming Zhao · 2024

In order to make full use of the advantages of artificial bee colony algorithm (ABC) and differential evolution algorithm (DE), a hybrid optimization algorithm based on differential evolution algorithm and artificial bee colony algorithm (HODEABC) is proposed in this paper. Firstly, we improve the strategy of the DE and ABC algorithm, utilizing two strategies based on the neighborhood information and the whole population information to adapt the strategy according to the improvement rate. Secondly, we employ a hybrid algorithm based on population division to evaluate performance based on the accumulated fitness value, and allocate more computing resources to algorithms demonstrating superior performance. Finally, compared with the improved algorithm on the CEC2014 test set and 22 standard function test sets, the results show that the performance of the proposed algorithm is comparable to these improved algorithms.

Cited by 0