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An efficient e-learning recommendation system for user preferences using hybrid optimization algorithm

SOFT COMPUTING 2021年第14期25卷 9377-9388页

作者： Vedavathi, N. Anil Kumar, K. M. NIE Inst Technol Dept Comp Sci & Engn Mysore Karnataka India Sri Jayachamarajendra Coll Engn Dept Comp Sci & Engn Mysore Karnataka India

The expanding approval of e-learning structure has made the need for the customized suggestion prototype which can be utilized to advance the successful learning condition for the learners. Customized suggestion model is a particular sort of data separating framework used to recognize a lot of articles that are applicable to a e-learners. In this paper, we mainly propose the efficient e-learning recommendation (EELR) system for user preferences using hybrid optimization algorithm (HOA). EELR system constructs a HOA with deep recurrent neural network (DRNN) and improved whale optimization (IWO) algorithm. First, DRNN is utilized to order the e-learner types dependent on these e-learner gatherings, clients can acquire course proposal from the gathering's persuasion. Thereafter, the conduct and the inclinations of the learners are examined via completing the mining of the arrangements watched every now and again by the IWO calculation. Rather than a learner effectively looking for data, recommender frameworks give counsel to students about articles they may wish to analyze. At last, the proposal of the e-learning depends on the appraisals comparing to these arrangements watched often. This proposed system is going to implement and validate in numerous e-learning entries against the client inclinations over some undefined time frame and demonstrated to be more proficiency and exactness contrasted with the customary recommender framework. This strategy can help learners to grasp the knowledge system and learning direction, and improve their learning efficiency. Observation results show that the proposed methodology empowers the asset suggestion to singular clients, which is started from different sources.

关键词： Efficient e-learning recommendation hybrid optimization algorithm Deep recurrent neural network Improved whale optimization

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An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems

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INFORMATION SCIENCES 2021年 561卷 304-325页

作者： Pan, Jeng-Shyang Liu, Nengxian Chu, Shu-Chuan Lai, Taotao Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China Minjiang Univ Coll Comp & Control Engn Fuzhou Peoples R China

Surrogate-assisted evolutionary algorithms (SAEAs) are potential approaches to solve computationally expensive optimization problems. The critical idea of SAEAs is to combine the powerful searching capabilities of evolutionary algorithms with the predictive capabilities of surrogate models. In this study, an efficient surrogate-assisted hybrid optimization (SAHO) algorithm is proposed via combining two famous algorithms, namely, teaching-learning-based optimization (TLBO) and differential evolution (DE). The TLBO is focused on global exploration and the DE is concentrated on local exploitation. These two algorithms are carried out alternately when no better candidate solution can be found. Meanwhile, a new prescreening criterion based on the best and top collection information is introduced to choose promising candidates for real function evaluations. Besides, two evolution control (i.e., the generation-based and individual-based) strategies and a top-ranked restart strategy are integrated in the SAHO. Moreover, a local RBF surrogate which does not need too many training samples is employed to model the landscapes of the target function. Sixteen benchmark functions and the tension/compression spring design problem are adopted to compare the proposed SAHO with other state-of-the-art approaches. Extensive comparison results demonstrate that the proposed SAHO has superior performance for solving expensive optimization problems. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Surrogate-assisted hybrid optimization algorithm Teaching-learning-based optimization Differential evolution Expensive problems

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An intelligent intrusion detection system for distributed denial of service attacks: A support vector machine with hybrid optimization algorithm based approach

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第27期34卷

作者： Sokkalingam, Sumathi Ramakrishnan, Rajesh Univ VOC Coll Engn Dept Comp Sci & Engn Thoothukudi 628008 Tamil Nadu India Indian Inst Technol Madras Dept Engn Design Chennai Tamil Nadu India

Cloud computing offers comfortable service to business sectors as they can concentrate on their products. Over the internet, cloud computing is liable to various security threats and attacks which is a primary obstacle to the growth of cloud computing services. Distributed denial of service (DDoS) is one such attack that exploits cloud computing services using compromised machines;hence, its detection is a significant field of research. Several DDoS detection schemes have been proposed in the past, but they fail to detect real-time active DDoS attacks because of their growth in severity and volume. Machine learning (ML) techniques are efficient in making predictions;hence, in this study, a hybrid ML intrusion detection system (IDS) model is proposed. The performance of the proposed IDS model is improved by employing a 10-fold cross-validation technique to perform feature selection, reducing data dimensions on the publicly available benchmark NSL-KDD dataset. Performance validation of the proposed hybrid IDS model is done using the confusion matrix. Support vector machine (SVM) parameters are fine-tuned using hybrid Harris Hawks optimization (HHO) and particle swarm optimization (PSO) algorithms. The performance of these hybrid algorithms is compared with other classical algorithms such as C4.5, K-nearest neighbor, and SVM using performance metrics such as precision, sensitivity, specificity, F1 score, and accuracy. From these comparisons, it can be inferred that the proposed SVM with hybrid optimization HHO-PSO machine learning IDS model performs better DDoS detection with good performance metric values.

关键词： distributed denial of service hybrid optimization algorithm intrusion detection system performance metrics support vector machine

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Lithium Battery Remaining Useful Life Prediction Based on Multi-Kernel Support Vector Regression With hybrid optimization algorithm

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JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE 2022年第3期19卷 031006-031006页

作者： Li, Hao Fu, Lijun Zhang, Yan Naval Univ Engn Natl Key Lab Sci & Technol Vessel Integrated Powe Wuhan 430033 Hubei Peoples R China

Accurately and reliably predicting the remaining useful life (RUL) of lithium battery is very important for the lithium battery health management system. However, most of the existing methods rely on complex multidimensional input features, which require a large number of sensors, increase the application cost and introduce redundant measurement errors. Therefore, this paper, only based on the battery capacity curve itself, proposes a method to construct a prediction model of support vector regression (SVR) by fusing multiple kernel functions. The linear equation coefficients of multiple kernel function combinations are optimized by the hybrid optimization algorithm. It is found that the hybrid kernel function can effectively overcome the problem that the single-kernel function is not capable of mapping the capacity fading trend of lithium battery. hybrid optimization algorithm can avoid the problems of local optimization and global search ability deficiency. The proposed method is validated by experiments using the battery attenuation datasets from NASA, the University of Maryland, and a high-rate lithium battery in the laboratory stage. It can be seen from the experimental results that the prediction accuracy of this method is high. The mean prediction error, mean RMSE, and mean MAE are 2%, 0.0198, and 0.0157.

关键词： lithium battery hybrid optimization algorithm multi-kernel support vector regression (MSVR) remaining useful life prediction reliability durability and damage tolerance

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Traffic travel service selection based on hybrid optimization algorithm 21

Traffic travel service selection based on hybrid optimizatio...

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Proceedings of the 3rd World Symposium on Software Engineering

作者： Wanchun Yang Chenxi Zhang School of Sciences Shandong Jiaotong University China School of Software Engineering Tongji University China

ISBN: (纸本)9781450384094

With the development of information technology in transportation industry, it becomes an important application of intelligent transportation system to provide real-time and effective travel services. The existing traffic travel service composition model is based on the assumption of single request, and it can not build composition according to granularity. Aiming at the problem of requests and granularity, from the two dimensions of transaction and quality of service (QoS), this paper presents an evaluation model of service composition which considers concurrent requests and multi-granularity. Based on the model, the paper proposed a method based on hybrid optimization algorithm. The proposed algorithm combined particle swarm optimization, crossover and mutation operators with priority, simulated annealing algorithm to get the optimal value. Experimental results show that our approach can guarantee QoS of service selection with low time cost.

关键词： Multi-granularity Transactional property Service selection hybrid optimization algorithm Quality of service(QoS) Concurrent requests

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E²MR-HOA: Conservation of Energy through Multi-Hop Routing Protocol for WSN'S Using hybrid optimization algorithm

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2021年第3期30卷

作者： Kumaran, G. Yaashuwanth, C. Sri Venkateswara Coll Engn Autonomous Dept Informat Technol Chennai Tamil Nadu India

Consuming energy at the maximal level is a major concern in wireless sensor networks (WSNs). Many researchers focus on reducing and preserving the energy. The duration of active network of WSNs is affected by energy consumption of sensor nodes. For typical applications such as structure monitoring, border surveillance, integrated into the external surface of a pipeline, and clambered along the sustaining structure of a bridge, sensor node energy efficiency is an important issue. The paper proposed an energy-efficient multi-hop routing protocol using hybrid optimization algorithm ((EMR)-M-2-HOA) for WSNs. The proposed routing protocol consists of two algorithms, i.e., hybrid optimization algorithm. We present modified chemical reaction optimization (MCRO) algorithm to form clusters and select cluster head (CH) among the cluster members. Then the modified bacterial forging search (MBFS) algorithm is used to compute reliable route between source to destination. The proposed (EMR)-M-2-HOA protocol is evaluated using NS2 simulations. The simulation result shows that the proposed routing protocol provides significant energy efficiency with network lifetime over the existing routing protocols.

关键词： Energy efficient multi-hop hybrid optimization algorithm clusters cluster head

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A Novel hybrid optimization algorithm Combined with BBO and ACO 39

A Novel Hybrid Optimization Algorithm Combined with BBO and ...

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39th Chinese Control Conference (CCC)

作者： Dai, Zhuo Ma, Qian Zhao, Dongdong Yan, Shi Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

ISBN: (纸本)9789881563903

This paper proposes a novel hybrid optimization algorithm based biogeography-based optimization (BBO) and ant colony optimization (ACO). Specifically, BBO is utilized for preliminary search and then the final search is conducted by using ACO on the preliminary results, which makes the hybrid algorithm have powerful search ability in the solution space. Moreover, the algorithm is tested on 6 well-known benchmarks for comparing with the 4 others existing optimization algorithms, including 2 algorithms were proposed by the coauthor earlier. Obviously, the simulation results show that the proposed hybrid BBO-ACO has better performance than the existing others.

关键词： hybrid optimization algorithm Biogeography-Based optimization Ant Colony optimization

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Robust optimization for a nonlinear switched time-delay system with noisy output measurements using hybrid optimization algorithm

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2019年第16期356卷 9730-9762页

作者： Yuan, Jinlong Xie, Jun Liu, Chongyang Teo, Kok Lay Huang, Ming Fan, Houming Feng, Enmin Xiu, Zhilong Dalian Maritime Univ Sch Sci Dept Math Dalian 116026 Liaoning Peoples R China Dalian Maritime Univ Coll Transportat Engn Dalian 116026 Liaoning Peoples R China PLA Dalian Naval Acad Dept Basics Teaching & Res Off Math Dalian 116018 Liaoning Peoples R China Shandong Inst Business & Technol Sch Math & Informat Sci Yantai 264005 Peoples R China Curtin Univ Dept Math & Stat Perth WA 6102 Australia Tianjin Univ Finance & Econ Coordinated Innovat Ctr Computable Modeling Manag Tianjin 300222 Peoples R China Dalian Univ Technol Sch Math Sci Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Sch Life Sci & Biotechnol Dalian 116024 Liaoning Peoples R China

This paper considers the problem of using noisy output data to estimate unknown switching times and unknown system parameters arising in 1,3-PD batch production process of glycerol induced by Klebsiella pneumonia. We formulate the problem as a robust optimization problem in which the unknown quantities are decision variables to be chosen optimally, with the cost function penalizing the mean and variance of the relative error between the output of the system and the measured actual noisy system output. This problem is governed by a switched time-delay system subject to continuous state inequality constraints arising from engineering specifications. A new time-scaling transformation and constraint transcription technique are used then to convert this resulting problem into a sequence of approximate subproblems. A hybrid optimization algorithm combined genetic algorithm with gradient-based method is developed to solve these resulting subproblems. Finally, we explore the correctness of the optimal switching times and system parameters obtained, as well as the effectiveness of the proposed algorithm. (C) 2019 Published by Elsevier Ltd on behalf of The Franklin Institute.

关键词： Nonlinear programming Robust optimization Switched time-delay system Noisy output data hybrid optimization algorithm

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A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 85卷 254-268页

作者： Zhang, Zichen Ding, Shifei Jia, Weikuan China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Minist Educ Peoples Republ China Mine Digitizat Engn Res Ctr Xuzhou 221116 Jiangsu Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 265000 Shandong Peoples R China

Based on Cuckoo Search (CS) and Differential Evolution (DE), a novel hybrid optimization algorithm, called CSDE, is proposed in this paper to solve constrained engineering problems. CS has strong ability on global search and less control parameters, but easy to suffer from premature convergence and lower the density of population. DE specializes in local search and good robustness, however, its convergence rate is too late to find the satisfied solution. Furthermore, these two algorithms are both proved to be especially suitable for engineering problems. This work divides population into two subgroups and adopts CS and DE for these two subgroups independently. By division, these two subgroups can exchange useful information and these two algorithms can utilize each other's advantages to complement their shortcoming, thus avoid premature convergence, balance the quality of solution and the computation consumption, and find satisfactory global optima. Due to the tremendous design variables and constrained conditions of engineering problems, single optimizer failed to meet the requirement of precision, so hybrid optimization algorithms (such like CSDE) is the most promising mean to complete this job. Simulation results reveal that CSDE has more ability to find promising results than other 12 algorithms (including traditional algorithms and state-of-the-art algorithm) on 30 unconstrained benchmark functions, 10 constrained benchmark functions and 6 constrained engineering problems.

关键词： hybrid optimization algorithm Cuckoo search Differential evolution Constrained engineering problems

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A Novel hybrid optimization algorithm Combined with BBO and ACO

A Novel Hybrid Optimization Algorithm Combined with BBO and ...

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第三十九届中国控制会议

作者： Zhuo Dai Qian Ma Dongdong Zhao Shi Yan School of Information Science and Engineering Lanzhou University

This paper proposes a novel hybrid optimization algorithm based biogeography-based optimization(BBO) and ant colony optimization(ACO).Specifically,BBO is utilized for preliminary search and then the final search is conducted by using ACO on the preliminary results,which makes the hybrid algorithm have powerful search ability in the solution ***,the algorithm is tested on 6 well-known benchmarks for comparing with the 4 others existing optimization algorithms,including2 algorithms were proposed by the coauthor ***,the simulation results show that the proposed hybrid BBO-ACO has better performance than the existing others.

关键词： hybrid optimization algorithm Biogeography-Based optimization Ant Colony optimization

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