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Architecture and Algorithm of Formation control of Multi-unmanned vehicles Based on Swarm intelligence
The main contents of this dissertation are the application of swarm intelligence to multi-unmanned vehicle system and the realization of cooperative control of multi-unmanned vehicle system. Firstly, this thesis introduces the research background and significance of multi-unmanned vehicle system and swarm intelligence. Secondly, we introduce the research contents of cooperative control of multi-unmanned vehicle system. Then the research status of cooperative control of multi-unmanned vehicle system in the domestic and overseas are introduced. Finally, this dissertation summarizes the mainstream types of architecture and algorithm of the multi-unmanned vehicle system.
Journal Article
The Hamiltonian Properties of Rectangular Meshes with at Most Two Faulty Nodes
In this paper we consider the Hamilton cycle problem in the rectangular meshes with at most two faulty nodes. We prove that this problem is solvable in polynomial time with a corresponding algorithm. We provided an entirely new approach to this problem being different from the method early used on this problem.
Journal Article
Improved SVM classification algorithm based on KFCM and LDA
To address the problem that SVM is sensitive to outliers and noise points, in order to improve the classification accuracy of SVM, this paper introduces fuzzy theory and intraclass dispersion theory, proposes an improved SVM classification algorithm, uses KFCM and LDA to filter the data set, and selects reasonable training samples, thereby reducing the number of wild points and noise points in the training sample, and thus reducing its impact on the classification effect of the classification model. Compared with the traditional SVM, the algorithm in this paper considers the impact of training samples on the classification effect, introduces fuzzy theory and intra-class dispersion, and eliminates the wild points and noise points in the training samples that affect the classification accuracy of the classification model. Experimental verification shows that the classification accuracy of the SVM classification model trained by the filtered training samples is higher than that of the SVM classification model without the trained training samples.
Journal Article
Evolutionary algorithms and their applications to engineering problems
The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms.
Journal Article
Analysis Effect of Tournament Selection on Genetic Algorithm Performance in Traveling Salesman Problem (TSP)
This study discusses effect of tournament selection on the way individuals compete on the performance of Genetic Algorithms so which one tournament selection is most suitable for the Traveling Salesman Problem (TSP). One algorithm in solving TSP is Genetic Algorithm, which has 3 (three) main operators, namely selection, crossover, and mutation. Selection is one of the main operators in the Genetic Algorithm, where select the best individuals who can survive (the shortest travel route). Tournament selection compares a number of individuals through a match to choose the best individual based on each fitness value, so that the winning individual (the individual going to the next generation) will be chosen. There is two way to compete in an individual in tournament selection is by tournament selection with replacement (TSWR) and without replacement (TSWOR). The final results of the study conducted TSWR gets the best fitness, even though the generation that gets the best fitness is reaching the maximum generation (takes longer to get the best fitness).
Journal Article
Prediction of slope failure in open-pit mines using a novel hybrid artificial intelligence model based on decision tree and evolution algorithm
In this study, the objective was to develop a new and highly-accurate artificial intelligence model for slope failure prediction in open-pit mines. For this purpose, the M5Rules algorithm was combined with a genetic algorithm (GA) in a novel hybrid technique, named M5Rules–GA model, for slope stability estimation and analysis and 450-slope observations in an open-pit mine in Vietnam were modeled using the Geo-Studio software based on essential parameters. The factor of safety was used as the model outcome. Artificial neural networks (ANN), support vector regression (SVR), and previously introduced models (such as FFA-SVR, ANN-PSO, ANN-ICA, ANN-GA, and ANN-ABC) were also developed for evaluating the proposed M5Rules–GA model. The evaluation of the model performance involved applying and computing the determination coefficient, variance account for, and root mean square error, as well as a general ranking and color scale. The results confirmed that the proposed M5Rules–GA model is a robust tool for analyzing slope stability. The other investigated models yielded less robust performance under the evaluation metrics.
Journal Article
Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods
Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas.
Journal Article
Analysis of music influence on order preference based on TOPSIS algorithm
Music, as a cultural treasure, is currently difficult to accurately measure its influence due to its special nature. In order to solve this problem, an evaluation model based on TOPSIS is proposed. The influence of music is defined as the combination of the number of followers and the number of followers of the same genre as the influencer, and then the TOPSIS method is used to quantitatively analyze the influence of music. By establishing the original data matrix of relevant music characteristics, and then forward and normalize the data matrix, calculate the distance between the data and the optimal solution and the worst solution, and obtain the quantitative score of the music influence value, a right Accurate measurement of music influence is realized.
Journal Article
KF-Swoosh: An Efficient Spark-Based Entity Resolution Algorithm for BigData
Entity matching (EM), which is, the task of identifying records that refer to the same entity, is a critical task when constructing data warehouses. This task is often very expensive at the running time because data must be compared in pairs. This problem becomes more important when dealing with large-scale data. We propose a new parallel algorithm that divides the data using K-Medoid algorithm implemented with Spark framework. The computational experiments are done and show that we can improve the solution of a set of instances in a reduced execution time.
Journal Article