To solve the Set Covering Problem we will use a metaheuristic fireworks algorithm inspired by the fireworks explosion. Through the observation of the way that fireworks explode is much similar to the way that an indiv...
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ISBN:
(纸本)9783319420851
To solve the Set Covering Problem we will use a metaheuristic fireworks algorithm inspired by the fireworks explosion. Through the observation of the way that fireworks explode is much similar to the way that an individual searches the optimal solution in swarm. fireworks algorithm (FWA) consists of four parts, i.e., the explosion operator, the mutation operator, the mapping rule and selection strategy. The Set Covering Problem is a formal model for many practical optimization problems. It consists in finding a subset of columns in a zero/one matrix such that they cover all the rows of the matrix at a minimum cost.
The proposed system represents an enhanced version in search of food of Sand Cat based on Levy distribution and firework algorithm for the image classification of grape leaf diseases. In the preprocessing step, the pr...
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ISBN:
(纸本)9798350391749
The proposed system represents an enhanced version in search of food of Sand Cat based on Levy distribution and firework algorithm for the image classification of grape leaf diseases. In the preprocessing step, the proposed system utilizes convolution kernels to transform images into input data within the range of (0,1). Successively, the Levy distribution and firework algorithm are incorporated into the SCSO model as an exploration search mechanism. The study employed a grape leaf dataset sourced from the Plant Village project (***), comprising 4062 labeled images measuring 256 by 256 pixels and categorized into 4 distinct classes: healthy, Black Rot, Black Measles, and Isariopsis leaf spot, which was utilized to evaluate the efficacy of the proposed system. The experimental findings demonstrate that the proposed system outperforms the analyses of VGG16, GLCM with SVM, Low contrast haze reduction-neighborhood component analysis with SVM, and SCSO.
This paper presents a multi-objective vibration-based particle-swarm-optimization (MO-VBPSO) algorithm with enhanced exploration ability and convergence performance, for training fuzzy-controller (FC) to achieve robot...
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ISBN:
(纸本)9781728185262
This paper presents a multi-objective vibration-based particle-swarm-optimization (MO-VBPSO) algorithm with enhanced exploration ability and convergence performance, for training fuzzy-controller (FC) to achieve robot control. The MO-VBPSO applies a reference point-based leader selection schema that assigns leaders for MO-PSOs' searching optimal parameters of the FC. Besides, the MO-VBPSO framework is integrated with a vibration factor to strengthen the exploration ability for resolving the local minima issue, which is inspired by the amplitude of the firework algorithm (FWA). The evaluation of MO-VBPSO focuses on the effect of the vibration factor by applying it to training a mobile robot in a simulation environment. The evaluation results are discussed concerning exploration ability, convergence performance, and performance stability. Experimental results reveal that the proposed MO-VBPSO lifts the performance of robot training significantly.
Pole assignment optimization is important for improving system stability. This paper considers the regional pole assignment optimization based on the swam intelligence optimization algorithm. Regular pole assignment c...
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Pole assignment optimization is important for improving system stability. This paper considers the regional pole assignment optimization based on the swam intelligence optimization algorithm. Regular pole assignment cannot overcome the effect of system disturbance and cannot reach the best control performance. And the firework algorithm (FWA) often ignore valuable local search opportunity and its selection mechanism taken much computation effort. Therefore, a Dynamic Collaborative fireworks algorithm (DCFWA) which reserves the advantages of fireworks algorithm (FWA) is presented for solve these problems, and a few features of the proposed algorithm as follows. First, a new explosion radius scaling strategy is designed by adjusting the scaling coefficient according to the distribution of optimal value points, which effectively enhances the optimal firework's search capability. Second, a new offspring fireworks selection method is built for avoiding the algorithm falling into local optimum. Third, a new initialization method is embedded into the algorithm for improving global search capability. Some well-known algorithms and our proposed algorithm are applied to several types of pole assignment optimization of control systems. The comparison results indicate that the proposed algorithm does outperform the other ones. (c) 2020 Elsevier B.V. All rights reserved.
In recent years, the number of satellites in orbit has increased rapidly, which provides an effective guarantee for multiple satellites to complete earth observation missions jointly. In order to solve the multi-satel...
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In recent years, the number of satellites in orbit has increased rapidly, which provides an effective guarantee for multiple satellites to complete earth observation missions jointly. In order to solve the multi-satellite joint observations planning problem(MSJOPP), a task splitting method is used to allocate tasks to satellites for execution effectively. Firstly, a mixed-integer programming(MIP) multi-satellite joint observation model is constructed, which fully considers the capabilities of resources for observing. After that, in order to plan these two types of tasks effectively, a two-stage hybrid planning algorithm(TSA) is proposed. TSA consists of an improved firework algorithm(FWA-I) and a heuristic algorithm(HA). In the first stage, FWA-I is used to process the inseparable observation task sequences. HA is used to plan the separable task sequences in the second stage. Finally, simulation experiments verify algorithms and the task splitting method. The task splitting method can effectively improve the performance of observation task planning.
Aiming at the problem that it is difficult to accurately judge the working state parameters during the grinding process of the ball mill, a method for predicting the working state parameters of the ball mill based on ...
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Aiming at the problem that it is difficult to accurately judge the working state parameters during the grinding process of the ball mill, a method for predicting the working state parameters of the ball mill based on fireworks algorithm optimized LSSVM (Least Squares Support Vector Machine) is proposed. Firstly, the LSSVM algorithm is employed to establish the predictive model for the working state parameters of the ball mill. Then, the FWA (firework algorithm) algorithm is used to optimize the radial basis kernel function parameters and penalty factors of the LSSVM model. Afterwards, time-domain features, frequency-domain features, and entropy features are extracted from the vibration signals of the mill shell to generate a set of feature vectors; finally, feature vectors are used as the input of FWA-LSSVM, and the ratio of material to ball, rotation speed and filling rate are used as the output to establish a mill state parameter prediction model. The superiority of the method is proved by grinding experiments. The results showed that the LSSVM model optimized with the FWA algorithm had less error between the predicted and actual values of filling speed, Material-ball ratio and rotational speed than the GA (Genetic algorithm) and PSO (Particle Swarm Optimization) optimization algorithms, indicating that the mill state parameter prediction model has higher precision and stability.
Shell vibration signals generated during grinding have useful information related to ball mill load, while usually contaminated by noises. It is a challenge to recognize load parameters with these signals. In this pap...
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Shell vibration signals generated during grinding have useful information related to ball mill load, while usually contaminated by noises. It is a challenge to recognize load parameters with these signals. In this paper, a novel approach is proposed based on the improved empirical wavelet transform (EWT), refined composite multi-scale dispersion entropy (RCMDE) and fireworks algorithm (FWA) optimized SVM. Firstly, vibration signals are denoised by improved EWT, which uses cubic spline interpolation to calculate envelope spectrum for segmentation. Then, RCMDEs of the denoised signals are calculated as feature vectors. The vectors' dimensionalities are reduced by principal component analysis (PCA). Finally, a mill load prediction model is established based on the FWA optimized SVM. The reduced feature vectors are fed to the model, thus material-to-ball ratio and filling rate being outputs. Grinding experiments show that the extracted features by RCMDE can effectively distinguish three load states. Meanwhile, experiments also show that FWA reduces the forecasting errors of material-to-balls ratio and filling rate by 1.9% and 2.9% compared with genetic algorithm (GA), as well as by 1.92% and 4.21% compared with particle swarm optimization (PSO) algorithm. It demonstrates that the proposed approach for ball mill load forecasting has high accuracy and stability.
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