Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00093
Huiyi Zhou, Yong Wang, Miao Ye
Nowadays, CNN widely used in network traffic classification. The traditional model of CNN only can be sent with the fixed traffic dataset in network traffic classification. But for the traffic dataset in network, that model must lead to a certain degree loss of the dataset by truncated or discarded. To solve this defect, a new CNN traffic classification model based on sppnet (spatial pyramid pooling) is proposed in this paper. Based on the CNN model of the LeNet-5, in the pooling layer before the fully connected layer, the new model is replaced the max-pooling to the spatial pyramid pooling which can realize the network traffic with indefinite length dataset. Through a series of experiments, the model has achieved certain achievement, and reducing the impact of human factors on traffic classification.
{"title":"A Method of CNN Traffic Classification Based on Sppnet","authors":"Huiyi Zhou, Yong Wang, Miao Ye","doi":"10.1109/CIS2018.2018.00093","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00093","url":null,"abstract":"Nowadays, CNN widely used in network traffic classification. The traditional model of CNN only can be sent with the fixed traffic dataset in network traffic classification. But for the traffic dataset in network, that model must lead to a certain degree loss of the dataset by truncated or discarded. To solve this defect, a new CNN traffic classification model based on sppnet (spatial pyramid pooling) is proposed in this paper. Based on the CNN model of the LeNet-5, in the pooling layer before the fully connected layer, the new model is replaced the max-pooling to the spatial pyramid pooling which can realize the network traffic with indefinite length dataset. Through a series of experiments, the model has achieved certain achievement, and reducing the impact of human factors on traffic classification.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121311576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00020
Xiaohua Li
In this paper, two evolutionary methods, Drosophila Optimization (DO) and Gravitational Search Algorithm (GSA), are compared. Important problem of evolutionary methods is how to balance exploitation and exploration. We take a set of numerical experiments to verify the performance of these methods. Numerical results show that GSA is better than DO in convergence rate or accuracy.
{"title":"The Comparing Analysis of Drosophila Optimization and Gravitational Search Algorithm","authors":"Xiaohua Li","doi":"10.1109/CIS2018.2018.00020","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00020","url":null,"abstract":"In this paper, two evolutionary methods, Drosophila Optimization (DO) and Gravitational Search Algorithm (GSA), are compared. Important problem of evolutionary methods is how to balance exploitation and exploration. We take a set of numerical experiments to verify the performance of these methods. Numerical results show that GSA is better than DO in convergence rate or accuracy.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115873504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00061
Changhong Zhu, Xiaoping Liang, Wei Deng
The traditional functional test system has such defects as high deviation and poor stability in the automotive ECU (Electronic Control Unit) test, so an automotive ECU functional test system based on PXI was proposed in the paper, wherein the system is composed of PXI bus, electronic monitoring test module, universal meter and computer. Specifically, the PXI bus is used for the data collection and the initialization test of the automotive ECU and for transmitting the data to the electronic monitoring test module; the electronic monitoring test module is composed of controller, power supply module, detection module, signal transceiver and on-off controller; the power module is used as the power supply of the electronic monitoring module; the detection module is used for transmitting the fault data detected thereby to the signal transceiver for signal transformation; the on-off controller is used for switching or cutting off the circuit through the fault signal analysis, and the controller is used for controlling the operation procedure of the whole electronic monitoring test module and for transmitting the screened data to the universal meter; the universal meter is used for amplifying the received data signals and transmitting the amplified data to the computer. The software design aimed to provide the test procedure and the troubleshooting algorithm of the automotive ECU functional test system based on PXI. The experiment result shows that the system designed thereby has high accuracy and high stability.
传统的功能测试系统在汽车ECU (Electronic Control Unit)测试中存在偏差大、稳定性差等缺陷，因此本文提出了一种基于PXI的汽车ECU功能测试系统，该系统由PXI总线、电子监控测试模块、通用仪表和计算机组成。其中，PXI总线用于汽车ECU的数据采集和初始化测试，并将数据传输给电子监控测试模块;电子监控测试模块由控制器、电源模块、检测模块、信号收发器和通断控制器组成;所述电源模块作为电子监控模块的电源;检测模块用于将其检测到的故障数据发送至信号收发器进行信号变换;通断控制器用于通过故障信号分析开关或切断电路，控制器用于控制整个电子监控测试模块的运行程序，并将筛选后的数据传输到万用表;通用仪表用于放大接收到的数据信号，并将放大后的数据传输到计算机。软件设计旨在提供基于PXI的汽车ECU功能测试系统的测试流程和故障排除算法。实验结果表明，所设计的系统精度高，稳定性好。
{"title":"Automotive ECU Functional Test System Based on PXI","authors":"Changhong Zhu, Xiaoping Liang, Wei Deng","doi":"10.1109/CIS2018.2018.00061","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00061","url":null,"abstract":"The traditional functional test system has such defects as high deviation and poor stability in the automotive ECU (Electronic Control Unit) test, so an automotive ECU functional test system based on PXI was proposed in the paper, wherein the system is composed of PXI bus, electronic monitoring test module, universal meter and computer. Specifically, the PXI bus is used for the data collection and the initialization test of the automotive ECU and for transmitting the data to the electronic monitoring test module; the electronic monitoring test module is composed of controller, power supply module, detection module, signal transceiver and on-off controller; the power module is used as the power supply of the electronic monitoring module; the detection module is used for transmitting the fault data detected thereby to the signal transceiver for signal transformation; the on-off controller is used for switching or cutting off the circuit through the fault signal analysis, and the controller is used for controlling the operation procedure of the whole electronic monitoring test module and for transmitting the screened data to the universal meter; the universal meter is used for amplifying the received data signals and transmitting the amplified data to the computer. The software design aimed to provide the test procedure and the troubleshooting algorithm of the automotive ECU functional test system based on PXI. The experiment result shows that the system designed thereby has high accuracy and high stability.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"284 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122963226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00091
Qing Wu, Haoyi Zhang, Rongrong Jing, Zhicang Wang
In order to improve the classification ability of the Twin Support Vector Machine (TWSVM), a new class of twice continuously differentiable piecewise smooth functions is used to smooth the objective function of unconstrained TWSVM and a class of smooth piecewise twin support vector machine (SPTWSVMd) is proposed. It is shown that the approximation accuracy and smoothness rank of piecewise functions can be as high as required. In order to reduce the influence of noise, the membership function is defined according to the distance between the sample points of each class and its intra-class hyperplane and a class of fuzzy SPTWSVMd (FSPTWSVMd) is proposed. The FSPTWSVMd can efficiently handle large scale and high dimensional problems based on the reduced kernel technique. The effectiveness of the proposed method is demonstrated via experiments on NDC datasets.
{"title":"A Class of Fuzzy Smooth Piecewise Twin Support Vector Machine","authors":"Qing Wu, Haoyi Zhang, Rongrong Jing, Zhicang Wang","doi":"10.1109/CIS2018.2018.00091","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00091","url":null,"abstract":"In order to improve the classification ability of the Twin Support Vector Machine (TWSVM), a new class of twice continuously differentiable piecewise smooth functions is used to smooth the objective function of unconstrained TWSVM and a class of smooth piecewise twin support vector machine (SPTWSVMd) is proposed. It is shown that the approximation accuracy and smoothness rank of piecewise functions can be as high as required. In order to reduce the influence of noise, the membership function is defined according to the distance between the sample points of each class and its intra-class hyperplane and a class of fuzzy SPTWSVMd (FSPTWSVMd) is proposed. The FSPTWSVMd can efficiently handle large scale and high dimensional problems based on the reduced kernel technique. The effectiveness of the proposed method is demonstrated via experiments on NDC datasets.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124764310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00049
Xingsi Xue, Aihong Ren, Dongxu Chen
Since these biomedical ontologies are mostly developed independently and many of them cover overlapping domains, establishing meaningful links between them, so-called biomedical ontology matching, is critical to ensure inter-operability and has the potential to unlock biomedical knowledge by bridging related data. Due to the complexity of the biomedical ontology matching problem (large-scale optimal problem with lots of local optimal solutions), Evolutionary Algorithm (EA) can present a good methodology for determining biomedical ontology alignments. However, the slow convergence and premature convergence are two main shortcomings of EA-based ontology matching techniques, which make them incapable of effectively searching the optimal solution for biomedical ontology matching problems. To overcome this drawback, in this paper, an Evolutionary Tabu Search Algorithm (ETSA) is proposed, which introduces the Tabu Search algorithm (TS) as a local search strategy into EA's evolving process. Moreover, to efficiently solve the biomedical ontology matching problem, an biomedical concept similarity measure is presented to calculate the similarity value of two biomedical concepts and an optimal model for biomedical ontology matching is constructed. The experiment is conducted on the Large Biomed track provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with state-of-the-art ontology matchers show the effectiveness of ETSA.
{"title":"An Evolutionary Tabu Search Algorithm for Matching Biomedical Ontologies","authors":"Xingsi Xue, Aihong Ren, Dongxu Chen","doi":"10.1109/CIS2018.2018.00049","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00049","url":null,"abstract":"Since these biomedical ontologies are mostly developed independently and many of them cover overlapping domains, establishing meaningful links between them, so-called biomedical ontology matching, is critical to ensure inter-operability and has the potential to unlock biomedical knowledge by bridging related data. Due to the complexity of the biomedical ontology matching problem (large-scale optimal problem with lots of local optimal solutions), Evolutionary Algorithm (EA) can present a good methodology for determining biomedical ontology alignments. However, the slow convergence and premature convergence are two main shortcomings of EA-based ontology matching techniques, which make them incapable of effectively searching the optimal solution for biomedical ontology matching problems. To overcome this drawback, in this paper, an Evolutionary Tabu Search Algorithm (ETSA) is proposed, which introduces the Tabu Search algorithm (TS) as a local search strategy into EA's evolving process. Moreover, to efficiently solve the biomedical ontology matching problem, an biomedical concept similarity measure is presented to calculate the similarity value of two biomedical concepts and an optimal model for biomedical ontology matching is constructed. The experiment is conducted on the Large Biomed track provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with state-of-the-art ontology matchers show the effectiveness of ETSA.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114673603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00070
Shichang Xuan, Dapeng Man, Wei Wang, Kaiyue Qin, Wu Yang
This research paper introduces a Denoising auto encoder (Unsupervised Deep Neural Network) combined with a typical Back Propagation (BP) Artificial Neural Network (ANN), capable of efficiently detecting WEB Trojan malware. Several researchers in the literature, employ Machine Learning (ML) to detect WEB Trojans. The data used in this paper, come from the WEB security Gateway, since there is less tagged data than unlabeled ones. Based on the literature, simple Supervised Learning (SULE) is not efficient enough for this task. The algorithm proposed herein is hybrid. It employs Unsupervised Learning (UNLE) based on a Stack Denoising Auto encoder (SdAE) to pre-train the data (one layer at a time). This results in more robust feature vectors. Then, in the fine-tuning process, minor adjustments are made through Supervised Learning (SUL) based on a BP ANN. The proposed approach, ensures that the developed model, can still perform accurately, even when the training data set has a small number of tagged data vectors. This research, verifies this hybrid Deep Learning approach used for WEB Trojan detection, outperforms other common classification methods.
{"title":"Hybrid Classification of WEB Trojan Exploiting Small Volume of Labeled Data Vectors","authors":"Shichang Xuan, Dapeng Man, Wei Wang, Kaiyue Qin, Wu Yang","doi":"10.1109/CIS2018.2018.00070","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00070","url":null,"abstract":"This research paper introduces a Denoising auto encoder (Unsupervised Deep Neural Network) combined with a typical Back Propagation (BP) Artificial Neural Network (ANN), capable of efficiently detecting WEB Trojan malware. Several researchers in the literature, employ Machine Learning (ML) to detect WEB Trojans. The data used in this paper, come from the WEB security Gateway, since there is less tagged data than unlabeled ones. Based on the literature, simple Supervised Learning (SULE) is not efficient enough for this task. The algorithm proposed herein is hybrid. It employs Unsupervised Learning (UNLE) based on a Stack Denoising Auto encoder (SdAE) to pre-train the data (one layer at a time). This results in more robust feature vectors. Then, in the fine-tuning process, minor adjustments are made through Supervised Learning (SUL) based on a BP ANN. The proposed approach, ensures that the developed model, can still perform accurately, even when the training data set has a small number of tagged data vectors. This research, verifies this hybrid Deep Learning approach used for WEB Trojan detection, outperforms other common classification methods.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128228556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/cis2018.2018.00118
Dongyi He, Wenxue Niu
The practical teaching mode based on human resources professional competition, which can promote the cultivation of active practice ability of human resources management students, enable students to learn and practice voluntarily, and improve the analytical ability, innovation ability and practical ability of human resources management students, as well as, improve the quality of personnel training, will have better application and promotion value.
{"title":"Research on the Practical Teaching Mode Based on Human Resources Professional Competition","authors":"Dongyi He, Wenxue Niu","doi":"10.1109/cis2018.2018.00118","DOIUrl":"https://doi.org/10.1109/cis2018.2018.00118","url":null,"abstract":"The practical teaching mode based on human resources professional competition, which can promote the cultivation of active practice ability of human resources management students, enable students to learn and practice voluntarily, and improve the analytical ability, innovation ability and practical ability of human resources management students, as well as, improve the quality of personnel training, will have better application and promotion value.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117278909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00036
Xunzhang Li, Yong Wang, Wenlong Ke, Hao Feng
In recent years, with the rapid development of the Internet, the data scale of application behavior and application traffic have exploded. How to classify the real-time traffic of network becomes a big challenge. How to balance the accuracy and real-time of traffic classification is a difficult problem in technology. Therefore, this paper proposes a pattern matching real-time traffic classification method named PM, which first uses jpcap to accept network traffic data in real time, and then uses pattern matching to perform real-time matching traffic characteristics to achieve traffic classification. Among them, the use of the distributed message system kafka and the parallel computing framework Spark significantly improve the execution efficiency of the program. The experimental results show that PM has good performance in terms of accuracy.
{"title":"Real-Time Network Traffic Classification Based on CDH Pattern Matching","authors":"Xunzhang Li, Yong Wang, Wenlong Ke, Hao Feng","doi":"10.1109/CIS2018.2018.00036","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00036","url":null,"abstract":"In recent years, with the rapid development of the Internet, the data scale of application behavior and application traffic have exploded. How to classify the real-time traffic of network becomes a big challenge. How to balance the accuracy and real-time of traffic classification is a difficult problem in technology. Therefore, this paper proposes a pattern matching real-time traffic classification method named PM, which first uses jpcap to accept network traffic data in real time, and then uses pattern matching to perform real-time matching traffic characteristics to achieve traffic classification. Among them, the use of the distributed message system kafka and the parallel computing framework Spark significantly improve the execution efficiency of the program. The experimental results show that PM has good performance in terms of accuracy.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132872501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00065
Weitao Ha
This paper addresses an cloud service selection model which supports to customize evaluation attributes dynamically. Using expanding the OWL-S Ontology, Cloud service QoS semantics is constructed. The weight of attribute is obtained by the objective and subjective synthetic approach. Based on fuzzy theory and artificial immune network, a new data classification method, named Fuzzy C-Means artificial immune network memory classifier (FCMAINMC), is put forward. According the algorithm, memory antibody collection in which characteristics of service are condensed is abstracted, and each service (antigen) that belongs to some type is also obtained. Using membership matrix and a hundred-mark way, evaluation result which reflects Web quality of service is obtain. The prototype is designed. It is applied to case evaluation fruitfully, and the experiment results are veracious and reliable as well as stable.
{"title":"Cloud Service Selection with Fuzzy C-Means Artificial Immune Network Memory Classifier","authors":"Weitao Ha","doi":"10.1109/CIS2018.2018.00065","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00065","url":null,"abstract":"This paper addresses an cloud service selection model which supports to customize evaluation attributes dynamically. Using expanding the OWL-S Ontology, Cloud service QoS semantics is constructed. The weight of attribute is obtained by the objective and subjective synthetic approach. Based on fuzzy theory and artificial immune network, a new data classification method, named Fuzzy C-Means artificial immune network memory classifier (FCMAINMC), is put forward. According the algorithm, memory antibody collection in which characteristics of service are condensed is abstracted, and each service (antigen) that belongs to some type is also obtained. Using membership matrix and a hundred-mark way, evaluation result which reflects Web quality of service is obtain. The prototype is designed. It is applied to case evaluation fruitfully, and the experiment results are veracious and reliable as well as stable.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131343308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/CIS2018.2018.00087
Ding Wang
Focusing on the disadvantages of gravitation search algorithm (GSA) and artificial bee colony (ABC), such as low convergence precision, slow convergence, and this paper proposed a new hybrid optimization algorithm NHA based on GSA and ABC. NHA balance exploitation and exploration. Numerical experiments show that NHA has good results in high dimensions.
{"title":"A New Hybrid Global Optimization Algorithm","authors":"Ding Wang","doi":"10.1109/CIS2018.2018.00087","DOIUrl":"https://doi.org/10.1109/CIS2018.2018.00087","url":null,"abstract":"Focusing on the disadvantages of gravitation search algorithm (GSA) and artificial bee colony (ABC), such as low convergence precision, slow convergence, and this paper proposed a new hybrid optimization algorithm NHA based on GSA and ABC. NHA balance exploitation and exploration. Numerical experiments show that NHA has good results in high dimensions.","PeriodicalId":185099,"journal":{"name":"2018 14th International Conference on Computational Intelligence and Security (CIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122203203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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