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A modified hybrid rice optimization algorithm for solving 0-1 knapsack problem

APPLIED INTELLIGENCE 2022年第5期52卷 5751-5769页

作者： Shu, Zhe Ye, Zhiwei Zong, Xinlu Liu, Shiqin Zhang, Daode Wang, Chunzhi Wang, Mingwei Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Hubei Univ Technol Sch Mech Engn Wuhan Peoples R China

The 0-1 knapsack problem (KP) is a classic NP-hard problem and could be handled by swarm intelligence algorithms. However, most of these algorithms might be trapped in the local optima as the scale increases. hybrid rice optimization (HRO) is a novel swarm intelligence algorithm inspired by the breeding process of Chinese three-line hybrid rice, its population is classified into three types such as the maintainer, restorer and sterile line and several stages including hybridization, selfing and renewal are implemented. In this paper, a modified HRO algorithm is proposed for the complicated large-scale 0-1 KP. A dynamic step is introduced in the renewal stage to balance the exploration and exploitation phases. Moreover, HRO is combined with binary ant colony optimization (BACO) algorithm to compose the parallel model and serial model for enhancing the convergence speed and search efficiency. In the parallel model, HRO and BACO are independently implemented on two subpopulations and communicate during each iteration. In the serial model, BACO is embedded in HRO as an operator to update the maintainer line. The experimental results on 0-1 KPs of different scales and correlations demonstrate the outperformance of the parallel model and serial model.

关键词： 0-1 knapsack problem Swarm intelligence algorithm hybrid rice optimization algorithm Binary ant colony optimization algorithm

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Hyperparameter optimization for Capsule Network Based Modified hybrid rice optimization algorithm

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Intelligent Automation & Soft Computing 2023年第8期37卷 2019-2035页

作者： Zhiwei Ye Ziqian Fang Zhina Song Haigang Sui Chunyan Yan Wen Zhou Mingwei Wang School of Computer Science and Technology Hubei University of TechnologyWuhan430068China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhan430079China

Hyperparameters play a vital impact in the performance of most machine learning *** is a challenge for traditional methods to con-figure hyperparameters of the capsule network to obtain high-performance *** swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a com-binatorial optimization ***,these algorithms are prone to get trapped in the local optimal solution as random search strategies are *** inspiration for the hybrid rice optimization(HRO)algorithm is from the breeding technology of three-line hybrid rice in China,which has the advantages of easy implementation,less parameters and fast *** the paper,genetic search is combined with the hybrid rice optimization algorithm(GHRO)and employed to obtain the optimal hyperparameter of the capsule network automatically,that is,a probability search technique and a hybridization strategy belong with the primary *** benchmark functions are used to evaluate the performance of ***,the MNIST,Chest X-Ray(pneumonia),and Chest X-Ray(COVID-19&pneumonia)datasets are also utilized to evaluate the capsule network learnt by *** experimental results show that GHRO is an effective method for optimizing the hyperparameters of the capsule network,which is able to boost the performance of the capsule network on image classification.

关键词： Hyperparameter optimization hybrid rice optimization algorithm genetic algorithm capsule network image classification

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Security Situation Prediction based on hybrid rice optimization algorithm and Back Propagation Neural Network 4

Security Situation Prediction based on Hybrid Rice Optimizat...

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4th IEEE International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS)

作者： Zhang, Xu Ye, Zhiwei Yan, Lingyu Wang, Chunzhi Wang, Ruoxi Hubei Univ Technol Sch Comp Sci Wuhan Hubei Peoples R China Wuhan FiberHome Tech Serv Co Ltd Wuhan Hubei Peoples R China

ISBN: (纸本)9781538675878

Research on network security situation awareness is currently a research hotspot in the field of network security. It is one of the easiest and most effective methods to use the BP neural network for security situation prediction. However, there are still some problems in BP neural network, such as slow convergence rate, easy to fall into local extremum, etc. On the other hand, some common used evolutionary algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO), easily fall into local optimum. hybrid rice optimization algorithm is a newly proposed algorithm with strong search ability, so the method of this paper is proposed. This article describes in detail the use of BP network security posture prediction method. In the proposed method, HRO is used to train the connection weights of the BP network. Through the advantages of HRO global search and fast convergence, the future security situation of the network is predicted, and the accuracy of the situation prediction is effectively improved.

关键词： Security Situation Prediction hybrid rice optimization algorithm BP neural network

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A Network Intrusion Detection Method Based on hybrid rice optimization algorithm Improved Fuzzy C-Means 4

A Network Intrusion Detection Method Based on Hybrid Rice Op...

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4th IEEE International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS)

作者： Jin, Can Ye, Zhiwei Wang, Chunzhi Yan, Lingyu Wang, Ruoxi Hubei Univ Technol Sch Comp Sci Wuhan 430068 Hubei Peoples R China Wuhan FiberHome Tech Serv Co Ltd Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781538675878

Fuzzy C-means (FCM) is a classical clustering method for data analysis in the machine learning field which has been successfully applied in the network intrusion detection. But it is sensitive to the initial clustering center, isolated point and noise, as well it is easy to trap into the local optimal solution. There are some methods for alleviating this problem by using optimization algorithms such as genetic algorithm, particle swam optimization algorithm, grey wolf optimization algorithm etc. However, in general, there are multiple local optimal values in the objective function of FCM, which is not fully conquered with evolutionary algorithms. A newly proposed hybrid rice optimization algorithm is employed to improve the basic FCM(HROFCM) and applied for network intrusion detection. Finally, KDD' 99 data is used to test the effectiveness of the method and experimental results show that the performance of HROFCM prevails in the comparison of GAFCM(fuzzy c-means based on genetic algorithm) and GWOFCM (fuzzy c-means based on grey wolf optimization algorithm).

关键词： Network intrusion detection Fuzzy c-means Clustering hybrid rice optimization algorithm

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A hybrid rice optimization algorithm with Ant System for Feature Selection 11

A Hybrid Rice Optimization Algorithm with Ant System for Fea...

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11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Ye, Zhiwei Shu, Zhe Liu, Shiuin Xia, Xiaoyu Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781665426053

Feature selection is an important processing step of data mining and aims at selecting a subset with high quality. The feature subset is obtained by reducing the irrelevant and redundant features based on maintaining satisfactory accuracy. Many algorithms might be trapped in local optima on solving high-dimensional feature selection. hybrid rice optimization (HRO) is a newly presented algorithm, its population is divided into three types such as the maintainer, restorer, and sterile line to simulate the breeding process of Chinese hybrid rice. Three main stages are designed to update the restorer and sterile line. However, the maintainer line is not updated in HRO, which degrades the performance of the algorithm on high-dimensional feature selection. In this paper, a hybrid model named HROAS is proposed for feature selection by combining HRO with ant system (AS). AS is embedded as an operator in HRO to update the maintainer line due to its unique updating mechanism. The performance of HROAS is evaluated on nine standard benchmark datasets and compared with several classic and novel algorithms. The experimental results demonstrate the outperformance of HROAS.

关键词： feature selection hybrid rice optimization algorithm ant system

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A hybrid rice optimization algorithm 11

A Hybrid Rice Optimization Algorithm

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11th International Conference on Computer Science & Education (ICCSE)

作者： Ye, Zhiwei Ma, Lie Chen, Hongwei Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781509022182

An optimization algorithm called hybrid rice optimization algorithm (HRO) is proposed in the paper which is inspired by the breeding process of three line hybrid rice. The three line are sterile line, maintainer line, restorer line. The sterile line is hybridized with the maintainer line to produce the hybrid rice. The restorer line keeps the fine properties by self. The process of hybridization is an evolutionary process and the process of self is swarm search process. The two procedures affect each other and are combined with an appropriate proportion. Six benchmark functions have been employed to test the performance of HRO. In addition, particle swarm optimization algorithm (PSO), differential evolution algorithm(DE), genetic algorithm (GA), artificial bee colony (ABC) are also carried out to compare with the HRO in the same running environment. Experimental results on some benchmark problems verifies that HRO outperforms other optimization algorithms involved in the paper with respect to convergence and the speed of calculation on the whole

关键词： hybrid rice optimization algorithm Function optimization Swarm intelligence Evolutionary computation

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A Band Selection Method for Hyperspectral Image Based on Binary Coded hybrid rice optimization algorithm 11

A Band Selection Method for Hyperspectral Image Based on Bin...

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11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Ye, Zhiwei Liu, Shiqin Zong, Xinlu Shu, Zhe Xia, Xiaoyu Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781665426053

Hyperspectral image analysis is one of the most important topics in the field of remote sensing. As the band dimension of hyperspectral image increases, it may cause a curse of dimensionality. Band selection could choose a subset of bands that is the most effective for classification recognition to achieve dimensionality reduction. Furthermore, binary coding is more suitable for solving the band selection problem. Therefore, a band selection method for hyperspectral image based on binary coded hybrid rice optimization algorithm is proposed in the paper. The band selection problem is solved as a combinatorial optimization problem by defining an objective function based on the classification accuracy and the number of bands. The proposed method is compared with other nature-inspired algorithms on Indian Pines, Salinas, Kennedy Space Center, and Pavia University hyperspectral datasets. Experimental results demonstrate that the proposed method achieves satisfactory results in terms of performance and execution time for band selection.

关键词： hyperspectral image band selection hybrid rice optimization algorithm image processing

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Improved K-Means algorithm Based on hybrid rice optimization algorithm 9

Improved K-Means Algorithm Based on Hybrid Rice Optimization...

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9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Liu, Chuan Wang, Chunzhi Hu, Jixiong Ye, Zhiwei Hubei Univ Technol Sch Comp Sci Wuhan Hubei Peoples R China

ISBN: (纸本)9781538606971

Clustering analysis is an active research branch in the area of data mining due to its simplicity and rapidity. However, K-means algorithm has the shortcomings of heavily depending on the initial clustering center and easily falls into local optimum. In this paper, we consider a deep research on K-means algorithm of optimization. We put forward the first selected initial clustering center of K-means algorithm, toward this end, a novel hybrid algorithm based on K-means algorithm and hybrid rice optimization algorithm were proposed to rapidly find the optimal cluster centers and avoid getting into local optimum. Experimental results show that the proposed clustering algorithm outperforms other similar algorithms.

关键词： Clustering analysis K-means algorithm hybrid rice optimization algorithm

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