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IEEE International Conference on Information and Automation 2015

作者： Qi, Rongzhi Wang, Zhijian Ping, Ping Li, Shuiyan Hohai Univ Coll Comp & Informat Nanjing Jiangsu Peoples R China Hohai Univ Coll Sci Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781467391047

Pairwise testing is an effective combinatorial test generation technique that can generate relative small test suite to cover all pairs of parameter values at least once. Genetic algorithm has been used for pairwise test suite generation by some researchers. In order to improve the performance of genetic algorithm, this paper proposes a hybrid optimization algorithm by augmenting genetic algorithm with two-stage hill climbing. The first stage is to improve all the individuals after genetic operations. The second stage is to improve the best solution of the current generation at the end of each generation. A series of experiments are conducted to evaluate the proposed algorithm. Experiment results show that the proposed algorithm is very competitive with respect to other approaches reported in the literature.

关键词： pairwise testing hybrid optimization algorithm genetic algorithm hill climbing

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An effective hybrid optimization algorithm for the flow shop scheduling problem

An effective hybrid optimization algorithm for the flow shop...

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IEEE International Conference on Information Acquisition

作者： Sun Kai Yang Genke Shanghai Jiao Tong Univ Dept Automat Shanghai 200030 Peoples R China

ISBN: (纸本)1424405289

This paper presents a new multi-swarm co-evolutionary algorithm named parallel particle swam optimization (PPSO) on the basis of standard PSO algorithm. Simulated annealing (SA) algorithm was introduced to increase escaping probability from local optima. By reasonably combining the PPSO with SA, we develop a general, fast and easily implemented hybrid optimization algorithm, and apply it to solve flow shop scheduling problem. Comparing results indicate that the new hybrid method is an effective and competitive approach for the flow shop scheduling problem.

关键词： parallel particle swarm optimization simulated annealing hybrid optimization algorithm flow shop scheduling problem

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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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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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A novel combined model based on hybrid optimization algorithm for electrical load forecasting

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APPLIED SOFT COMPUTING 2019年 82卷 105548-000页

作者： Wang, Rui Wang, Jiyang Xu, Yunzhen Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Macau Univ Sci & Technol Fac Informat Technol Macau 999078 Peoples R China Lanzhou Univ Sch Basic Med Sci Lanzhou 730000 Gansu Peoples R China

Accurate electrical load forecasting always plays a vital role in power system administration and energy dispatch, which are the foundation of the smooth operation of the national economy and people's daily life. Thinking from this vision, many scholars have made great efforts to seek suitable optimization algorithms to improve the performance of existing forecasting algorithm. However, most of the studies ignore the inherent disadvantages of single optimization algorithm, which leads to suboptimal forecasting performance. Therefore, a novel electric load forecasting system was successfully proposed in this paper by the combination of data preprocessing, hybrid optimization algorithms, and several single classical forecasting methods, which successfully overcomes the defects of single traditional forecasting models and achieves higher forecasting accuracy than that of single model optimization. Besides, the 30 min interval data of Queensland, Australia from March to April is used as illustrative examples to evaluate the performance of the developed model. The results of tests demonstrate that the proposed hybrid model can better approximate the actual value, and it can also be employed as a useful tool for smart grids dispatching planning. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Electrical load forecasting Combined model Data preprocessing technique Classical forecasting methods hybrid optimization algorithm

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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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The application of hybrid optimization algorithm in joint inversion of surface and borehole magnetic data

The application of hybrid optimization algorithm in joint in...

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The 4th International Conference on Environmental and Engineering Geophysics

作者： Dalian Zhang,Tianyou Liu Institute of Geophysics and Geomatics,China University of Geosciences,Wuhan,430074,China

Global optimization algorithm has strong generality without the use of problem's specific information,which resulted in a waste of information on known *** optimization algorithms has strong dependence of the problem,but the utilization of information on specific problems can be quickly constructed a solution,its time performance is more *** optimization algorithm combines the advantages of the local optimization algorithm and global optimization algorithm and overcomes their *** can not only increase the computing speed,but also have a very good effect on improving the quality of *** Swarm optimization(PSO) is a new efficient and parallel optimization algorithm,this algorithm has a profound intelligence background,and is simple and easy to *** paper gives out the process of the hybrid optimization inversion which combined PSO algorithm with the singular value truncated method,and creates a vertical cuboid as the model,then uses this hybrid optimization method for the joint inversion of surface and borehole magnetic data in the theoretical model tests and the practical example of Daye exhausted *** result shows that the relative error of this hybrid optimization algorithm is smaller than the above two kinds of algorithms,and the computing time is between the two *** order to find the balance of time-consuming and precision,using hybrid algorithm is superior than using the two single algorithm.

关键词： hybrid optimization algorithm PSO Singular Value Truncated Method Joint Inversion

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A Novel hybrid optimization algorithm Combined with BBO and PSO

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

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第28届中国控制与决策会议

作者： Gang Cheng Chao Lv Shi Yan Li Xu School of Information Science and Engineering Lanzhou University Department of Electronics and Information Systems Akita Prefectural University

ISBN: (纸本)9781467397155

In this paper,a novel hybrid optimization algorithm named BBO-PSO is proposed,which is a combination of biogeography-based optimization(BBO) and particle swarm optimization(PSO).In this optimization algorithm,BBO will be employed for local search while PSO will be employed for global search,which makes the algorithm possess powerful search ability in the solution *** proposed algorithm and 3 existing optimization algorithms are tested on 6 well known benchmarks and the simulation results show that BBO-PSO has better performance than the others.

关键词： hybrid optimization algorithm Particle Swarm optimization Biogeography-Based optimization

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A new chaotic hybrid cognitive optimization algorithm

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COGNITIVE SYSTEMS RESEARCH 2018年 52卷 537-542页

作者： Zhou, Yuhong Su, Ke Shao, Limin Hebei Agr Univ Coll Mech & Elect Engn Baoding Peoples R China

To solve the optimization problems in port planning and operation management, particle swarm optimization, Cat mapping, and cloud model were combined. A Chaos Cloud Particle Swarm optimization (CCPSO) algorithm was proposed. It was used in port planning management.. Its application in port throughput forecasting and berthing and pontoon bridge allocation was explored and studied. By analyzing the mixed properties of Cat maps, the chaotic characteristics of the map were good. Thus, it was introduced into the hybrid optimization algorithm for chaotic perturbation of poor individuals in a particle swarm. The selection of the parameter combination of the Gauss-SVAR model was troublesome. The parameter combination of Guass-vSVR model was optimized by CCPSO algorithm, and the Guass-vSVR-CCPSO model was obtained. Using CCPSO algorithm, a discrete berth bridge allocation model was established. The results showed that the particle feasible integer processing module was developed. Therefore, a new method for multi-objective discrete berth shore-bridge allocation based on CCPSO algorithm is feasible. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Chaos Particle swarm optimization (PSO) hybrid optimization algorithm

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Interactive search algorithm: A new hybrid metaheuristic optimization algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2018年 71卷 275-292页

作者： Mortazavi, Ali Togan, Vedat Nuhoglu, Ayhan Usak Univ Civil Engn Dept Usak Turkey Karadeniz Tech Univ Civil Engn Dept Trabzon Turkey Ege Univ Civil Engn Dept Izmir Turkey

In this paper, a new hybrid optimization algorithm, called "Interactive Search algorithm (ISA)" is proposed for the solution of the optimization problems. This algorithm modifies and combines affirmative features of two developed metaheuristic methods called Integrated Particle Swarm optimization (iPSO) and Teaching and Learning Based optimization (TLBO). ISA consists of two separate paradigms: (i) Tracking and (ii) Interacting. Tracking paradigm utilizes the information stored in the current agent's memory and two other important agents, the weighted and best agents, to guide the colony. On the other hand, interacting paradigm provides a pairwise interaction between agents to share their knowledge with each other. Each agent based on its tendency factor employs one of these two paradigms in each cycle of ISA to explore the search space. Additionally, rather than conventional penalty approach, ISA utilizes the improved fly-back approach to handle problem constraints. The search capability of the proposed method is tested on the number benchmark mathematical functions and constrained mechanical design problems as the real-world examples. Consequently, the achieved numerical results demonstrate that the proposed method is competitive with other well-established metaheuristic methods.

关键词： hybrid optimization algorithm Metaheuristics Integrated particle swarm optimization Teaching and learning based optimization Numerical functions Mechanical design problem

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