Random Forest Parameter Optimization Method Based on Fireworks Algorithm

With respect to the nonlinear hypersonic vehicle (HV) dynamics, achieving a satisfactory tracking control performance under uncertainties is always a challenge. The high-order sliding mode control (HOSMC) method with strong robustness has been applied to HVs. However, there are few methods for determining suitable HOSMC parameters for an efficacious control of HV, given that the uncertainties are randomly distributed. In this study, we introduce a hybrid fireworks algorithm- (FWA-) based parameter optimization into HV control design to satisfy the design requirements with high probability. First, the complex relation between design parameters and the cost function that evaluates the likelihood of system instability and violation of design requirements is modeled via stochastic robustness analysis. Subsequently, we propose an efficient hybrid FWA to solve the complex optimization problem concerning the uncertainties. The efficiency of the proposed hybrid FWA-based optimization method is demonstrated in the search of the optimal HV controller, in which the proposed method exhibits a better performance when compared with other algorithms.

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A New Hyper-Parameter Optimization Method for Power Load Forecast Based on Recurrent Neural Networks

Algorithms ◽

10.3390/a14060163 ◽

2021 ◽

Vol 14 (6) ◽

pp. 163

Author(s):

Yaru Li ◽

Yulai Zhang ◽

Yongping Cai

Keyword(s):

Neural Networks ◽

Parameter Optimization ◽

Recurrent Neural Networks ◽

Critical Role ◽

Optimization Method ◽

Bayesian Optimization ◽

Power Load ◽

Gradient Calculation ◽

Truncated Newton ◽

Selection Of

The selection of the hyper-parameters plays a critical role in the task of prediction based on the recurrent neural networks (RNN). Traditionally, the hyper-parameters of the machine learning models are selected by simulations as well as human experiences. In recent years, multiple algorithms based on Bayesian optimization (BO) are developed to determine the optimal values of the hyper-parameters. In most of these methods, gradients are required to be calculated. In this work, the particle swarm optimization (PSO) is used under the BO framework to develop a new method for hyper-parameter optimization. The proposed algorithm (BO-PSO) is free of gradient calculation and the particles can be optimized in parallel naturally. So the computational complexity can be effectively reduced which means better hyper-parameters can be obtained under the same amount of calculation. Experiments are done on real world power load data,where the proposed method outperforms the existing state-of-the-art algorithms,BO with limit-BFGS-bound (BO-L-BFGS-B) and BO with truncated-newton (BO-TNC),in terms of the prediction accuracy. The errors of the prediction result in different models show that BO-PSO is an effective hyper-parameter optimization method.

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Parameter optimization method for the water quality dynamic model based on data-driven theory

Marine Pollution Bulletin ◽

10.1016/j.marpolbul.2015.07.004 ◽

2015 ◽

Vol 98 (1-2) ◽

pp. 137-147 ◽

Cited By ~ 4

Author(s):

Shuxiu Liang ◽

Songlin Han ◽

Zhaochen Sun

Keyword(s):

Water Quality ◽

Dynamic Model ◽

Parameter Optimization ◽

Optimization Method ◽

Data Driven ◽

Model Based

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Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10060420 ◽

2021 ◽

Vol 10 (6) ◽

pp. 420

Author(s):

Jun Wang ◽

Lili Jiang ◽

Qingwen Qi ◽

Yongji Wang

Keyword(s):

Parameter Optimization ◽

Scale Parameter ◽

Human Perception ◽

Optimization Methods ◽

Optimization Method ◽

Initiation Point ◽

Scale Parameters ◽

Object Based ◽

Geographic Object ◽

Multiresolution Segmentation

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

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Calibration of Conceptual Rainfall-Runoff Models Using Global Optimization

Advances in Meteorology ◽

10.1155/2015/545376 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 9

Author(s):

Chao Zhang ◽

Ru-bin Wang ◽

Qing-xiang Meng

Keyword(s):

Global Optimization ◽

Parameter Optimization ◽

Hydrological Model ◽

Optimization Method ◽

Difficult Problem ◽

Rainfall Runoff ◽

True Parameter ◽

Shuffled Complex Evolution ◽

Structure Lead ◽

Parameter Values

Parameter optimization for the conceptual rainfall-runoff (CRR) model has always been the difficult problem in hydrology since watershed hydrological model is high-dimensional and nonlinear with multimodal and nonconvex response surface and its parameters are obviously related and complementary. In the research presented here, the shuffled complex evolution (SCE-UA) global optimization method was used to calibrate the Xinanjiang (XAJ) model. We defined the ideal data and applied the method to observed data. Our results show that, in the case of ideal data, the data length did not affect the parameter optimization for the hydrological model. If the objective function was selected appropriately, the proposed method found the true parameter values. In the case of observed data, we applied the technique to different lengths of data (1, 2, and 3 years) and compared the results with ideal data. We found that errors in the data and model structure lead to significant uncertainties in the parameter optimization.

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A Parameter Optimization Method for Fingerprint-Based Indoor Localization System Using IEEE 802.15.4 Devices

2021 3rd International Conference on Computer Communication and the Internet (ICCCI) ◽

10.1109/iccci51764.2021.9486801 ◽

2021 ◽

Author(s):

Yuanzhi Huo ◽

Pradini Puspitaningayu ◽

Nobuo Funabiki ◽

Kazushi Hamazaki ◽

Minoru Kuribayashi ◽

...

Keyword(s):

Parameter Optimization ◽

Ieee 802.15.4 ◽

Indoor Localization ◽

Optimization Method ◽

Localization System

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Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN

10.4018/978-1-6684-2408-7.ch008 ◽

2022 ◽

pp. 166-201

Author(s):

Asha Gowda Karegowda ◽

Devika G.

Keyword(s):

Neural Networks ◽

Real Time ◽

Parameter Optimization ◽

Heuristic Algorithms ◽

Search Space ◽

Optimization Method ◽

Classification Problems ◽

Local Optima ◽

Consistent Solution ◽

Real Time Applications

Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters. This chapter will provide critics on both heuristics and meta-heuristic existing literature for training neural networks algorithms, applicability, and reliability on parameter optimization. In addition, the real-time applications of ANN will be presented. Finally, future directions to be explored in the field of ANN are presented which will of potential interest for upcoming researchers.

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Interval Type 2 Fuzzy Fireworks Algorithm for Clustering

Handbook of Research on Fireworks Algorithms and Swarm Intelligence - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-1659-1.ch009 ◽

2020 ◽

pp. 195-211

Author(s):

Juan Barraza ◽

Fevrier Valdez ◽

Patricia Melin ◽

Claudia I. Gonzalez

Keyword(s):

Fuzzy Inference ◽

Optimization Method ◽

Optimal Number ◽

Dynamic Adjustment ◽

Number Of Clusters ◽

Inference System ◽

Fireworks Algorithm ◽

Interval Type ◽

Optimal Number Of Clusters

This chapter presents Interval Type 2 Fuzzy Fireworks Algorithm for clustering (IT2FWAC). It is an optimization method for finding the optimal number of clusters based on the centroid features which uses the Fireworks Algorithm (FWA), but with a dynamic adjustment of parameters using an Interval Type 2 Fuzzy Inference System (IT2FIS). Three variations of the IT2FWAC are proposed to find the optimal number of clusters for different datasets: IT2FWAC -I, IT2FWAC -II, and IT2FWAC –III. They are explained in detail.

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A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm

Sensors ◽

10.3390/s20072147 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2147 ◽

Cited By ~ 3

Author(s):

Zhihang Yue ◽

Sen Zhang ◽

Wendong Xiao

Keyword(s):

Hybrid Algorithm ◽

Optimization Problems ◽

Optimization Method ◽

Grey Wolf Optimizer ◽

Local Optimum ◽

Grey Wolf ◽

Optimal Search ◽

Fireworks Algorithm ◽

Wide Range ◽

Search Capability

Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA.

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