scholarly journals Simultaneous Feature Selection and Support Vector Machine Optimization Using an Enhanced Chimp Optimization Algorithm

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 282
Author(s):  
Di Wu ◽  
Wanying Zhang ◽  
Heming Jia ◽  
Xin Leng
Keyword(s):  
Optimization Algorithm ◽  
Classification Accuracy ◽  
Search Algorithm ◽  
Rank Correlation ◽  
Population Diversity ◽  
The Other ◽  
Support Vector ◽  
Local Optimum ◽  
Original Algorithm ◽  
Spearman’S Rank Correlation

Chimp Optimization Algorithm (ChOA), a novel meta-heuristic algorithm, has been proposed in recent years. It divides the population into four different levels for the purpose of hunting. However, there are still some defects that lead to the algorithm falling into the local optimum. To overcome these defects, an Enhanced Chimp Optimization Algorithm (EChOA) is developed in this paper. Highly Disruptive Polynomial Mutation (HDPM) is introduced to further explore the population space and increase the population diversity. Then, the Spearman’s rank correlation coefficient between the chimps with the highest fitness and the lowest fitness is calculated. In order to avoid the local optimization, the chimps with low fitness values are introduced with Beetle Antenna Search Algorithm (BAS) to obtain visual ability. Through the introduction of the above three strategies, the ability of population exploration and exploitation is enhanced. On this basis, this paper proposes an EChOA-SVM model, which can optimize parameters while selecting the features. Thus, the maximum classification accuracy can be achieved with as few features as possible. To verify the effectiveness of the proposed method, the proposed method is compared with seven common methods, including the original algorithm. Seventeen benchmark datasets from the UCI machine learning library are used to evaluate the accuracy, number of features, and fitness of these methods. Experimental results show that the classification accuracy of the proposed method is better than the other methods on most data sets, and the number of features required by the proposed method is also less than the other algorithms.

scholarly journals An enhanced chimp optimization algorithm for continuous optimization domains

2021 ◽  
Author(s):  
Heming Jia ◽  
Kangjian Sun ◽  
Wanying Zhang ◽  
Xin Leng
Keyword(s):  
Social Status ◽  
Optimization Algorithm ◽  
Continuous Optimization ◽  
Rank Correlation ◽  
Function Optimization ◽  
Local Optimum ◽  
Hunting Behavior ◽  
Spearman’S Rank Correlation ◽  
Engineering Design Problems ◽  
Application Fields

AbstractChimp optimization algorithm (ChOA) is a recently proposed metaheuristic. Interestingly, it simulates the social status relationship and hunting behavior of chimps. Due to the more flexible and complex application fields, researchers have higher requirements for native algorithms. In this paper, an enhanced chimp optimization algorithm (EChOA) is proposed to improve the accuracy of solutions. First, the highly disruptive polynomial mutation is used to initialize the population, which provides the foundation for global search. Next, Spearman’s rank correlation coefficient of the chimps with the lowest social status is calculated with respect to the leader chimp. To reduce the probability of falling into the local optimum, the beetle antennae operator is used to improve the less fit chimps while gaining visual capability. Three strategies enhance the exploration and exploitation of the native algorithm. To verify the function optimization performance, EChOA is comprehensively analyzed on 12 classical benchmark functions and 15 CEC2017 benchmark functions. Besides, the practicability of EChOA is also highlighted by three engineering design problems and training multilayer perceptron. Compared with ChOA and five state-of-the-art algorithms, the statistical results show that EChOA has strong competitive capabilities and promising prospects.

scholarly journals Electricity Substitution Potential Prediction Based on Tent-CSO-CG-SSA-Improved SVM—A Case Study of China

2022 ◽  
Vol 14 (2) ◽  
pp. 853
Author(s):  
Jinqiang Geng ◽  
Weigao Meng ◽  
Qiaoran Yang
Keyword(s):  
Development Strategy ◽  
Search Algorithm ◽  
Electrical Power ◽  
Electrical Energy ◽  
Population Diversity ◽  
Fuel Oil ◽  
Support Vector ◽  
Local Optimum ◽  
Energy Substitution ◽  
Model Combining

Nowadays, fossil energy continues to dominate China’s energy usage; its inefficient use and large crude emissions of coal and fuel oil in its end-consumption have brought about great pressure to reduce emissions. Electrical power substitution as a development strategy is an important step toward achieving sustainable development, the transformation of the end-use energy consumption structure, and double carbon goals. To better guide the broad promotion of electrical power substitution, and to offer theoretical support for its development, this paper quantifies the amount of electrical power substitution and the influencing factors that affect the potential of electrical energy substitution. This paper proposes a hybrid model, combining Tent chaos mapping (Tent), chicken swarm optimization (CSO), Cauchy–Gaussian mutation (CG), the sparrow search algorithm (SSA), and a support vector machine (SVM), as a Tent-CSO-CG-SSA-SVM model, which first uses the method of Tent chaos mapping to initialize the sparrow population in order to increase population diversity and improve the search ability of the algorithm. Then, the CSO is introduced to update the positions of sparrows, and the CG method is introduced to make the algorithm jump out of the local optimum, in order to improve the global search ability of the SSA. Finally, the final electrical power substitution potential prediction model is obtained by optimizing the SVM through a multi-algorithm combination approach. To verify the validity of the model, two regions in China were used as case studies for the prediction analysis of electrical energy substitution potential, and the prediction results were compared with multiple models. The results of the study show that Tent-CSO-CG-SSA-SVM offers a good improvement in prediction accuracy, and that Tent-CSO-CG-SSA-SVM is a promising method for the prediction of electrical power substitution potential.

scholarly journals Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization

2021 ◽  
Vol 11 (16) ◽  
pp. 7358
Author(s):  
Linlin Li ◽  
Shufang Xu ◽  
Hua Nie ◽  
Yingchi Mao ◽  
Shun Yu
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Chaotic Map ◽  
Poor Performance ◽  
Population Diversity ◽  
Convergence Speed ◽  
Local Optimum ◽  
Search Problem ◽  
Target Search ◽  
Disturbance Factor

Unmanned aerial vehicles (UAVs) have shown their superiority in military and civilian missions. In the face of complex tasks, many UAVs are usually needed to cooperate with each other. Therefore, multi-UAV cooperative target search has attracted more and more scholars’ attention. At present, there are many bionic algorithms for solving the cooperative search problem of multi-UAVs, including particle swarm optimization algorithm (PSO) and differential evolution (DE). Pigeon-inspired optimization (PIO) is a new swarm intelligence optimization algorithm proposed in recent years. It has great advantages over other algorithms in convergence, robustness, and accuracy, and has few parameters to be adjusted. Aiming at the shortcomings of the standard pigeon colony algorithm, such as poor population diversity, slow convergence speed, and the ease of falling into local optimum, we have proposed chaotic disturbance pigeon-inspired optimization (CDPIO) algorithm. The improved tent chaotic map was used to initialize the population and increase the diversity of the population. The disturbance factor is introduced in the iterative update stage of the algorithm to generate new individuals, replace the individuals with poor performance, and carry out disturbance to increase the optimization accuracy. Benchmark functions and UAV target search model were used to test the algorithm performance. The results show that the CDPIO had faster convergence speed, better optimization precision, better robustness, and better performance than PIO.

scholarly journals Semisupervised SVM by Hybrid Whale Optimization Algorithm and Its Application in Oil Layer Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Yong-ke Pan ◽  
Ke-wen Xia ◽  
Wen-jia Niu ◽  
Zi-ping He
Keyword(s):  
Optimization Algorithm ◽  
Classification Accuracy ◽  
Semisupervised Learning ◽  
Convergence Speed ◽  
Unlabeled Data ◽  
The Other ◽  
Whale Optimization Algorithm ◽  
Support Vector ◽  
Model Parameters ◽  
Whale Optimization

In many fields, such as oil logging, it is expensive to obtain labeled data, and a large amount of inexpensive unlabeled data are not used. Therefore, it is necessary to use semisupervised learning to obtain accurate classification with limited labeled data and many unlabeled data. The semisupervised support vector machine (S3VM) is the most useful method in semisupervised learning. Nevertheless, S3VM model performance will degrade when the sample number of categories is not even or have lots of unlabeled samples. Thus, a new semisupervised SVM by hybrid whale optimization algorithm (HWOA-S3VM) is proposed in this paper. Firstly, a tradeoff control parameter is added in S3VM to deal with an uneven sample of category which can cause S3VM to degrade. Then, a hybrid whale optimization algorithm (HWOA) is used to optimize the model parameters of S3VM to increase the classification accuracy. For HWOA improvement, an opposition-based cubic mapping is used to initialize the WOA population to improve the convergence speed, and the catfish effect is used to help WOA jump out of the local optimum and obtain the global optimization ability. In the experiments, firstly, the HWOA is tested by 12 classic benchmark functions of CEC2005 and four functions of CEC2014 compared with the other five algorithms. Then, six UCI datasets are used to test the performance of HWOA-S3VM and compared with the other four algorithms. Finally, we applied HWOA-S3VM to perform oil layer recognition of three oil well datasets. These experimental results show that (1) HWOA has a higher convergence speed and better global searchability than other algorithms. (2) HWOA-S3VM model has higher classification accuracy on UCI datasets than other algorithms when combined, labeled, and unlabeled data are used as the training dataset. (3) The recognition accuracy and speed of the HWOA-S3VM model are superior to the other four algorithms when applied in oil layer recognition.

scholarly journals A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 757
Author(s):  
Yongke Pan ◽  
Kewen Xia ◽  
Li Wang ◽  
Ziping He
Keyword(s):  
Optimization Algorithm ◽  
Oil And Gas ◽  
The Other ◽  
Whale Optimization Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Recognition Model ◽  
Novel Approach ◽  
Whale Optimization ◽  
Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree

2013 ◽  
Vol 427-429 ◽  
pp. 1934-1938
Author(s):  
Zhong Rong Zhang ◽  
Jin Peng Liu ◽  
Ke De Fei ◽  
Zhao Shan Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.

scholarly journals A Quantitative Assessment of Pre-Operative MRI Reports in Glioma Patients: Report Metrics and IDH Prediction Ability

2021 ◽  
Vol 10 ◽  
Author(s):  
Hang Cao ◽  
E. Zeynep Erson-Omay ◽  
Murat Günel ◽  
Jennifer Moliterno ◽  
Robert K. Fulbright
Keyword(s):  
High Performance ◽  
Prediction Models ◽  
Rank Correlation ◽  
Support Vector ◽  
Wild Type ◽  
K Nearest Neighbors ◽  
Prediction Ability ◽  
Spearman’S Rank Correlation ◽  
Negative Findings ◽  
T1 Contrast

ObjectivesTo measure the metrics of glioma pre-operative MRI reports and build IDH prediction models.MethodsPre-operative MRI reports of 144 glioma patients in a single institution were collected retrospectively. Words were transformed to lowercase letters. White spaces, punctuations, and stop words were removed. Stemming was performed. A word cloud method applied to processed text matrix visualized language behavior. Spearman’s rank correlation assessed the correlation between the subjective descriptions of the enhancement pattern. The T1-contrast images associated with enhancement descriptions were selected. The keywords associated with IDH status were evaluated by χ2 value ranking. Random forest, k-nearest neighbors and Support Vector Machine algorithms were used to train models based on report features and age. All statistical analysis used two-tailed test with significance at p <.05.ResultsLonger word counts occurred in reports of older patients, higher grade gliomas, and wild type IDH gliomas. We identified 30 glioma enhancement descriptions, eight of which were commonly used: peripheral, heterogeneous, irregular, nodular, thick, rim, large, and ring. Five of eight patterns were correlated. IDH mutant tumors were characterized by words related to normal, symmetric or negative findings. IDH wild type tumors were characterized words by related to pathological MR findings like enhancement, necrosis and FLAIR foci. An integrated KNN model based on report features and age demonstrated high-performance (AUC: 0.89, 95% CI: 0.88–0.90).ConclusionReport length depended on age, glioma grade, and IDH status. Description of glioma enhancement was varied. Report descriptions differed for IDH wild and mutant gliomas. Report features can be used to predict glioma IDH status.

Feature Selection and Knapsack Problem Resolution Based on a Discrete Backtracking Optimization Algorithm

2021 ◽  
Vol 12 (2) ◽  
pp. 1-15
Author(s):  
Khadoudja Ghanem ◽  
Abdesslem Layeb
Keyword(s):  
Feature Selection ◽  
Knapsack Problem ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Knapsack Problems ◽  
Support Vector ◽  
Discrete Version ◽  
Search Optimization ◽  
Discrete Algorithms

Backtracking search optimization algorithm is a recent stochastic-based global search algorithm for solving real-valued numerical optimization problems. In this paper, a binary version of backtracking algorithm is proposed to deal with 0-1 optimization problems such as feature selection and knapsack problems. Feature selection is the process of selecting a subset of relevant features for use in model construction. Irrelevant features can negatively impact model performances. On the other hand, knapsack problem is a well-known optimization problem used to assess discrete algorithms. The objective of this research is to evaluate the discrete version of backtracking algorithm on the two mentioned problems and compare obtained results with other binary optimization algorithms using four usual classifiers: logistic regression, decision tree, random forest, and support vector machine. Empirical study on biological microarray data and experiments on 0-1 knapsack problems show the effectiveness of the binary algorithm and its ability to achieve good quality solutions for both problems.

scholarly journals Power load forecasting algorithm based on nonlinear inertial factor change pattern particle swarm optimization algorithm

2018 ◽  
Vol 173 ◽  
pp. 02016
Author(s):  
Jin Liang ◽  
Wang Yongzhi ◽  
Bao Xiaodong
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Support Vector ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Power Load ◽  
Selection Of

The common method of power load forecasting is the least squares support vector machine, but this method is very dependent on the selection of parameters. Particle swarm optimization algorithm is an algorithm suitable for optimizing the selection of support vector parameters, but it is easy to fall into the local optimum. In this paper, we propose a new particle swarm optimization algorithm, it uses non-linear inertial factor change that is used to optimize the algorithm least squares support vector machine to avoid falling into the local optimum. It aims to make the prediction accuracy of the algorithm reach the highest. The experimental results show this method is correct and effective.

A New Combined Particle Swarm Optimization Algorithm Based Golden Section Strategy

2011 ◽  
Vol 308-310 ◽  
pp. 1099-1105 ◽  
Author(s):  
Hui Fan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Evolution Model ◽  
The Other ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Global Evolution ◽  
Global Optimal

Based the defects of global optimal model falling into local optimum easily and local model with slow convergence speed during traditional PSO algorithm solving a complex high-dimensional and multi-peak function, a two sub-swarms particle optimization algorithm is proposed. All particles are divided into two equivalent parts. One part particles adopts global evolution model, while the other part uses local evolution model. If the global optimal fitness of the whole population stagnates for some iteration, a golden rule is introduced into local evolution model. This strategy can substitute the partial perfect particles of local evolution for the equivalent worse particles of global evolution model. So, some particles with advantage are joined into the whole population to make the algorithm keep active all the time. Compared with classic PSO and PSO-GL(A dynamic global and local combined particle swarm optimization algorithm, PSO-GL), the results show that the proposed PSO in this paper can get more effective performance over the other two algorithm in the simulation experiment for four benchmark testing function.

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