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Resource Allocation Based on Improved krill herd algorithm in Cloud Computing

淡江理工学刊 2019年第4期22卷 617-624页

作者： Jiaqi Wang

Resource allocation acts an important role in cloud computing. Traditional resource allocation methods have time-consuming, expensive and low efficiency disadvantages. Therefore, we propose an improved krill herd algorithm for resource allocation in cloud computing. Firstly, the status of cloud computing resource allocation is analyzed. Then, in order to prevent krill herd falling into local solution, we introduce random perturbation factor to improve the induction movement and foraging movement of krill herd. Finally, the experiments results show that the proposed method can effectively improve the system resource utilization, the allocation performance is better than other cloud computing resource allocation methods.

关键词： Cloud Computing Resource Allocation krill herd algorithm Random Perturbation

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Energy-aware virtual machines allocation by krill herd algorithm in cloud data centers

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HELIYON 2019年第7期5卷 e02066-e02066页

作者： Soltanshahi, Minoo Asemi, Reza Shafiei, Nazi Payame Noor Univ Dept Comp Sci & Engn Kerman Iran Dept Software Engn Sci & Res Kermanshah Branch Kermanshah Iran Islamic Azad Univ Dept Software Engn Kerman Iran

The growing demand for computational power has led to the emergence of large-scale data centers that consume massive amounts of energy, thus resulting in high operating costs and CO2 emission. Furthermore, cloud computing environments are required to provide a high Quality of Service (QoS) to their clients and, therefore, need to handle power shortages. An optimized virtual machine allocation to physical hosts lowers energy consumption and allows for high-quality services. In this study, a novel solution was proposed for the allocation of virtual machines to physical hosts in cloud data centers using the krill herd algorithm, which is the fastest collective intelligence algorithm recently introduced. The performance of the proposed method was evaluated using the CloudSim simulator, and the results are suggestive of a 35% reduction in energy consumption.

关键词： Computer science Green computing Cloud computing Virtualization Data center krill herd algorithm

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An improved krill herd algorithm based on meme grouping strategy of shuffled frog leaping algorithm

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2019年第2期46卷 156-162页

作者： Sun, Yu-Feng Wang, Jie-Sheng Ma, Xiao-Xu College of Information Science and Technology Northeastern University Shenyang110000 China School of International Finance and Banking University of Science and Technology Liaoning Anshan114051 China National Financial Security and System Equipment Engineering Research Center University of Science and Technology Liaoning China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

krill herd (KH) algorithm is a new bionic intelligent algorithm which originates from the behavior of foraging krill. In order to enhance the search performance of KH algorithm, and further improve the convergence speed of the algorithm and optimization precision, an improved krill herd optimization algorithm based on the memes grouping strategy of shuffled frog leaping algorithm was proposed. The simulation experiments are carried out by using the six typical test functions to discuss the optimization performance. The simulation results show that the improved krill herd algorithm can effectively increase the algorithm's convergence speed and optimization precision. © International Association of Engineers.

关键词： Function optimization krill herd algorithm Meme grouping Shuffled frog leaping algorithm

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HKF-SVR Optimized by krill herd algorithm for Coaxial Bearings Performance Degradation Prediction

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SENSORS 2020年第3期20卷 660-660页

作者： Liu, Fang Li, Liubin Liu, Yongbin Cao, Zheng Yang, Hui Lu, Siliang Anhui Univ Coll Elect Engn & Automat Hefei 230601 Peoples R China Anhui Univ Natl Engn Lab Energy Saving Motor & Control Techn Hefei 230601 Peoples R China

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF-SVR) whose parameters are optimized by krill herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF-SVR are optimized by the KH (krill herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF-SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

关键词： rolling bearing performance degradation hybrid kernel function krill herd algorithm SVR

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Research on Multi-objective Grid Fault Recovery Assistant Decision Based on krill herd algorithm 2019

Research on Multi-objective Grid Fault Recovery Assistant De...

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Proceedings of the 2nd International Conference on Information Technologies and Electrical Engineering

作者： Yupei Jia Mei Xie Jinxiu Hou Bo Fu Jingcheng Deng Power System Institute China Electric Power Research Institute Beijing China School of Electrical and Electronic Engineering Hubei University of Technology Wuhan Hubei China

ISBN: (纸本)9781450372930

After the fault of power grid, the dispatchers need to work out the most efficient recovery strategy in order to restore the smooth running of the grid in time through the comparison of relevant parameters. Moreover, the parameters involved in this paper are the ability of restoring loads, load importance, and the cost of switch operations, then, through the decision-making process of evaluating and comparing these parameters, the decision problem of the way to restore the grid fault is essentially transformed into a multi-objective optimization problem. And this paper introduces krill herd algorithm (KHA) related to the primary and secondary searching choices of herds for the targeted problem, meanwhile, taking every target parameter into full consideration, thus offers valuable suggestions to the dispatchers to eventually solve the problem. The feasibility of the method is verified by the analysis of IEEE39 nodes in this paper.

关键词： Multi-objective algorithm Service restoration Power system krill herd algorithm

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Multi-period Reliable Network Design of Fourth Party Logistics base on krill herd algorithm

Multi-period Reliable Network Design of Fourth Party Logisti...

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第32届中国控制与决策会议

作者： Jia Li Rui Li Yanqiu Liu School of Management Shenyang University of Technology

In order to make the Fourth Party Logistics（4 PL） system operate safely and efficiently in dynamic environment,the multi-period reliable network design of 4 PL with consideration of random disruptions is studied.A mathematical model is formulated,which maximizes the reliability of the 4 PL system subjective to investment *** on the NP-hard characteristic of the problem,the krill herd algorithm（KHA） and Artificial Fish Swarm algorithm（AFSA） are presented and the performance of the algorithm is tested by simulation *** to the simulation of MATLAB,the KHA performs better in effectiveness and stability than the *** makers can invest in redundant resources to improve the reliability of the 4 PL network considering the investment cost constraint.

关键词： Fourth party logistics Reliability optimization Network design krill herd algorithm

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Hybrid Monkey algorithm with krill herd algorithm Optimization for Feature Selection

Hybrid Monkey Algorithm with Krill Herd Algorithm Optimizati...

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International Computer Engineering Conference

作者： Ahmed Ibrahem Hafez Aboul Ella Hassanien Hossam M. Zawbaa E. Emary Faculty of Computer and Information Minia University Faculty of Computers and Information Beni-Suef University Faculty of Computers and Information Cairo University

ISBN: (纸本)9781509002764

In this work, a system for feature selection based on hybrid Monkey algorithm (MA) with krill herd algorithm (KHA) is proposed. Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attribute. A system for feature selection is proposed in this work using a hybrid Monkey algorithm and krill herd algorithm (MAKHA). The MAKHA algorithm adaptively balance the exploration and exploitation to quickly find the optimal solution. MAKHA is a new evolutionary computation technique, inspired by the chicken movement. The MAKHA can quickly search the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporate both classification accuracy and feature reduction size. The proposed system was tested on 18 data sets and proves advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.

关键词： Feature Selection Monkey algorithm krill herd algorithm Bio-inspired optimization Swarm Optimization

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Solution for a Kind of Dynamic Optimization Based on Improved krill herd algorithm

Solution for a Kind of Dynamic Optimization Based on Improve...

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2018 3rd International Conference on Modelling, Simulation and Applied Mathematics(MSAM 2018)

作者： Xingjun Yuan Yulei Ge Hao Lu Systems Engineering Research Institute College of Information and Control Engineering China University of Petroleum

Considering a kind of dynamic optimization, an improved krill herd(KH) algorithm which is called GSA-KH is proposed in this paper. The improvements consist of three parts: 1) a good point set is constructed to obtain the initial krill population which can promote the representativeness of the initial population;2) the speed factor is updated according to the changes of the krill population to accelerate the convergence;3) an adaptive Cauchy mutation is employed so that the algorithm can escape the local optimum reasonably. Simulations on six test functions illustrate that the convergence and accuracy of GSA-KH algorithm are increased greatly than standard KH algorithm. Then the proposed algorithm is applied to solving a dynamic optimization cases.

关键词： dynamic optimization krill herd algorithm adaptive cauchy mutation CVP method good point sets method speed factor

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Fuzzy krill herd Optimization algorithm 1

Fuzzy Krill Herd Optimization Algorithm

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1st International Conference on Networks & Soft Computing (ICNSC)

作者： Fattahi, Edris Bidari, Mandi Kanan, Hamidreza Rashidy Islamic Azad Univ Qazvin Branch Dept Elect Comp & IT Engn Qazvin Iran Islamic Azad Univ Qazvin Branch Dept Elect Biomed & Mechatron Engn Qazvin Iran

ISBN: (纸本)9781479934867

The Standard krill herd(SKH) optimization algorithm is one of the meta-heuristic algorithms which is proposed based on herding behavior of krill individuals in the nature for solving optimization problems. Considering that SKH is a meta-heuristic algorithm, two main properties of this algorithm is using mixture of random search or exploration and local search or exploitation. Keeping the exploration and exploitation of algorithm balanced plays crucial role in SKH to gain highest performance in solving optimization tasks. So, in this paper we have proposed fuzzy KH which is utilizing a fuzzy system as a parameter tuner for setting the participation amount of exploration and exploitation considering different conditions which may happen during solving the problems. We have tested the fuzzy KH algorithm on different benchmarks and the obtained results show the higher performance of proposed method.

关键词： meta-heuristic exploration-exploitation krill herd algorithm fuzzy controller

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Modified krill herd Optimization algorithm using Focus Group Idea 9

Modified Krill Herd Optimization Algorithm using Focus Group...

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9th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Bidar, Mahdi Fattahi, Edris Mouhoub, Malek Kanan, Hamidreza Rashidy Univ Regina Dept Comp Sci Regina SK Canada Islamic Azad Univ Dept Comp & Informat Technol Engn Marivan Iran Shahid Rajaee Teacher Training Univ Dept Comp Engn Tehran Iran

ISBN: (纸本)9789897582202

krill herd algorithm is one of most recently developed nature-inspired optimization algorithms which is inspired by herding behavior of krill individuals. In order to improve the performance of this algorithm to deal more effectively with high dimensional numerical functions, we propose a new method, called Focus Group idea to modify the solutions found by searching agents in group cooperation. In order to evaluate the effect of the proposed method on the performance of the krill herd algorithm, we conducted experiments on a set standard benchmark functions. The obtained results demonstrate the ability of the proposed method to improve the performance of the krill herd optimization algorithm.

关键词： Metaheuristics Evolutionary Computation krill herd algorithm Exploration Exploitation

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