A hybrid metaheuristic approach using random forest and particle swarm optimization to study and evaluate backbreak in open-pit blasting

2022 ◽  
Author(s):  
Yong Dai ◽  
Manoj Khandelwal ◽  
Yingui Qiu ◽  
Jian Zhou ◽  
M. Monjezi ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Random Forest ◽  
Particle Swarm ◽  
Open Pit ◽  
Swarm Optimization ◽  
Hybrid Metaheuristic

A hybrid feature selection algorithm combining ReliefF and Particle swarm optimization for high-dimensional medical data

2021 ◽  
pp. 1-15
Author(s):  
Zhaozhao Xu ◽  
Derong Shen ◽  
Yue Kou ◽  
Tiezheng Nie
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Random Forest ◽  
Classification Accuracy ◽  
Particle Swarm ◽  
Medical Data ◽  
High Dimensional ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Swarm Optimization

Due to high-dimensional feature and strong correlation of features, the classification accuracy of medical data is not as good enough as expected. feature selection is a common algorithm to solve this problem, and selects effective features by reducing the dimensionality of high-dimensional data. However, traditional feature selection algorithms have the blindness of threshold setting and the search algorithms are liable to fall into a local optimal solution. Based on it, this paper proposes a hybrid feature selection algorithm combining ReliefF and Particle swarm optimization. The algorithm is mainly divided into three parts: Firstly, the ReliefF is used to calculate the feature weight, and the features are ranked by the weight. Then ranking feature is grouped according to the density equalization, where the density of features in each group is the same. Finally, the Particle Swarm Optimization algorithm is used to search the ranking feature groups, and the feature selection is performed according to a new fitness function. Experimental results show that the random forest has the highest classification accuracy on the features selected. More importantly, it has the least number of features. In addition, experimental results on 2 medical datasets show that the average accuracy of random forest reaches 90.20%, which proves that the hybrid algorithm has a certain application value.

Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost

2019 ◽  
Vol 29 (2) ◽  
pp. 711-721 ◽  
Author(s):  
Xiliang Zhang ◽  
Hoang Nguyen ◽  
Xuan-Nam Bui ◽  
Quang-Hieu Tran ◽  
Dinh-An Nguyen ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Ground Vibration ◽  
Open Pit ◽  
Swarm Optimization ◽  
Open Pit Mines ◽  
Computing Model

scholarly journals Scenario-based wind speed estimation using a new hybrid metaheuristic model: Particle swarm optimization and radial movement optimization

2019 ◽  
Vol 52 (5-6) ◽  
pp. 493-508 ◽  
Author(s):  
Alper Kerem ◽  
Ali Saygin
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Artificial Neural Networks ◽  
Particle Swarm ◽  
System Stability ◽  
Training Process ◽  
Swarm Optimization ◽  
Hybrid Metaheuristic ◽  
Radial Movement ◽  
Artificial Neural

This paper presents a new hybrid metaheuristic model in order to estimate wind speeds accurately. The study was started by the training process of artificial neural networks with some metaheuristic algorithms such as evolutionary strategy, genetic algorithm, ant colony optimization, probability-based incremental learning, particle swarm optimization, and radial movement optimization in the literature. The success of each model is recorded in graphs. In order to make the closest estimation and to increase the system stability, a new hybrid metaheuristic model was developed using particle swarm optimization and radial movement optimization, and the training process of artificial neural networks was performed with this new model. The data were obtained by real-time measurements from a 63-m-high wind measurement station built at the coordinates of UTM E 263254 and N 4173479, altitude 1313 m. Two different scenarios were created using actual data and applied to all models. It was observed that the error values in the designed new hybrid metaheuristic model were lower than those of the other models.

scholarly journals A novel hybrid PSO-based metaheuristic for costly portfolio selection problems

2021 ◽  
Author(s):  
Marco Corazza ◽  
Giacomo di Tollo ◽  
Giovanni Fasano ◽  
Raffaele Pesenti
Keyword(s):  
Particle Swarm Optimization ◽  
Portfolio Selection ◽  
Particle Swarm ◽  
Selection Problem ◽  
Computational Time ◽  
Swarm Optimization ◽  
Portfolio Selection Problem ◽  
Hybrid Metaheuristic ◽  
Unconstrained Model ◽  
Unconstrained Problem

AbstractIn this paper we propose a hybrid metaheuristic based on Particle Swarm Optimization, which we tailor on a portfolio selection problem. To motivate and apply our hybrid metaheuristic, we reformulate the portfolio selection problem as an unconstrained problem, by means of penalty functions in the framework of the exact penalty methods. Our metaheuristic is hybrid as it adaptively updates the penalty parameters of the unconstrained model during the optimization process. In addition, it iteratively refines its solutions to reduce possible infeasibilities. We report also a numerical case study. Our hybrid metaheuristic appears to perform better than the corresponding Particle Swarm Optimization solver with constant penalty parameters. It performs similarly to two corresponding Particle Swarm Optimization solvers with penalty parameters respectively determined by a REVAC-based tuning procedure and an irace-based one, but on average it just needs less than 4% of the computational time requested by the latter procedures.

scholarly journals A new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm to estimate money demand in Iran

MethodsX ◽  
2021 ◽  
Vol 8 ◽  
pp. 101226
Author(s):  
Sayyed Abdolmajid Jalaee ◽  
Alireza Shakibaei ◽  
Hamid Reza Horry ◽  
Hossein Akbarifard ◽  
Amin GhasemiNejad ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Money Demand ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Hybrid Metaheuristic
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