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Balanced Job Scheduling Based on ant algorithm for Grid Network

INTERNATIONAL JOURNAL OF GRID AND HIGH PERFORMANCE COMPUTING 2010年第1期2卷 34-50页

作者： Preve, Nikolaos Tech Univ Athens Athens Greece

Job scheduling in grid computing is a very important problem. To utilize grids efficiently, we need a good job scheduling algorithm to assign jobs to resources in grids. The main scope of this article is to propose a new ant colony optimization (ACO) algorithm for balanced job scheduling in the Grid environment. To achieve the above goal, we will indicate a way to balance the entire system load while minimizing the makespan of a given set of jobs. Based on the experimental results, the proposed algorithm confidently demonstrates its practicability and competitiveness compared with other job scheduling algorithms.

关键词： ant colony optimization algorithm Grid Computing Heuristics Job Scheduling

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Research of Rural Power Network Reactive Power optimization Based on Improved ACOA

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Journal of Northeast Agricultural University(English Edition) 2010年第3期17卷 48-52页

作者： YU Qian ZHAO Yulin WANG Xintao Engineering College Northeast Agricultural University Harbin 150030 China Electric Power Design Institute Suihua Electric Power Design Institute Suihua 152061 Heilongjiang China

In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects： one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county＇s distribution network showed that the improved ACOA was practicable.

关键词： rural power network reactive power optimization ant colony optimization algorithm local search strategy pheromone mutation and re-initialization strategy

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A Comprehensive Study of Various Load Balancing Techniques used in Cloud Based Biomedical Services

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2015年第2期8卷 127-131页

作者： Hans, Abhinav Kalra, Sheetal GNDU RC Jalandhar Jalandhar Punjab India

With an increase in the demands, the Cloud computing has become one of the ongoing scalable approaches to fulfill the cloud based services. The biggest advantage of the cloud computing is the ability to overcome the infrastructural challenges those are earlier faced by other technologies. Since the technology is new, therefore the development of the whole structure is not so efficient. It does have a lot of issues on which various scientists and others are working on. Scheduling, load balancing, fault tolerance, etc. are various challenges faced by cloud computing. For this purpose various techniques and algorithms have been proposed. In this paper, we will discuss the issue of load balancing of cloud computing and we will study the different types of load balancing techniques used in in biomedical services and make a comparative analysis among all the existing techniques.

关键词： (OLB + LBMM) Genetic algorithm VectorDot Stochastic Hill Climbing LBVS- H ant colony optimization algorithm Compare and Balance Honey Bee Foraging

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Meta-heuristic algorithms for Double Roman Domination Problem

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APPLIED SOFT COMPUTING 2024年 154卷

作者： Aggarwal, Himanshu Reddy, P. Venkata Subba Natl Inst Technol Warangal Dept Comp Sci & Engn Hanamkonda 506004 Telangana India

A variety of domination concepts have been defined to provide better routing and defense strategies under different constraints. A double Roman dominating function (DROMDF) on a simple, undirected graph.. is a function g : V -> {0, 1, 2, 3} such that every vertex x is an element of V with g(x) = 0 is adjacent to at least two vertices y(1), y(2) with g(y(1)) = g(y(2)) = 2 or a vertex z(1) with g(z(1)) = 3. Also, a vertex p with g(p) = 1 is adjacent to at least one vertex q(1) with g(q(1)) >= 2. gamma(dR)(G), the double Roman domination number of G, is the smallest possible weight of all possible DROMDFs of G. Determining double Roman domination number of a graph is known to be NP-hard. Hence in this paper, we propose a genetic algorithm based approach for solving double Roman domination problem in which three heuristic algorithms have been proposed and problem specific crossover operator and a feasibility function has been developed. Further, we propose an ant colony optimization algorithm to solve double Roman domination problem. This paper provides an in-depth illustration of two algorithms for solving double Roman domination problem. Effectiveness of the proposed meta-heuristic algorithms is tested on the random graphs generated using NetworkX Erdos-Renyi model, a popular model for graph generation and Harwell-Boeing dataset, a well-known dataset for graph related problems. Further, we compare the results of both the meta-heuristic algorithms and the experimental results show that the proposed meta-heuristic algorithms for solving double Roman domination problem give a near optimal solution in reasonable time. Experimental results also show that the proposed ant colony optimization algorithm for solving double Roman domination problem outperforms genetic algorithm based procedure.

关键词： Dominating set Double roman domination NP-hard Genetic algorithm ant colony optimization algorithm

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Multi-Stage Partner Selection Based on Genetic-ant colony algorithm in Agile Supply Chain Network

Multi-Stage Partner Selection Based on Genetic-Ant Colony Al...

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The 9th International Conference for Young Computer Scientists(第九届国际青年计算机大会)

作者： Zheng Lin Lubin Wang Central University of Finance and Economics

It is shown that partner selection in agile supply chain network can be represented as a directed graph model. This paper considers multi-stages in this process instead of one. The natures of the proposed model enable us to design a meta-heuristic algorithm - GACA which combined the pros part of ACOA and GA for solving the good path search problem. Computational results show the efficiency of our algorithm in compare to the other techniques.

关键词： Partner selection genetic algorithm ant colony optimization algorithm agile supply chain network.

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ACO Optimizing Neural Network for Macroscopic Water Distribution System Modeling

ACO Optimizing Neural Network for Macroscopic Water Distribu...

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2010 International Conference on Intelligent Computing and Cognitive Informatics (ICICCI 2010)

作者： Hongxiang Wang North China University of Water Resources and Electric Power Zhengzhou,ChinaWenxian Guo North China University of Water Resources and Electric Power Zhengzhou,China

The artificial neural network (ANN) method is used to study the macroscopic model of an actual water distribution *** the first time,the ant colony optimization (ACO) algorithm is implemented to optimize the node numbers of the hidden layers in the ANN *** ANN model contains two hidden layers with a maximum of 64 nodes per *** node number in the hidden layers is transformed into a binary representation using Gray *** doing this,the logical structure of the ACO algorithm is altered from one of two decision points with sixty-four paths per point to one of twelve decision points with two options per *** newly defined logical structure makes better use of the parallel nature of the ACO *** preparations of the input data used in the ANN model are *** study indicates that the ANN method is an attractive alternative to the conventional regression analysis method in modeling water distribution systems.

关键词： Artificial Neural Network ant colony optimization algorithm Water Distribution System Modeling

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Path planning research based on the improved ant colony algorithm in ECDIS

Path planning research based on the improved ant colony algo...

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第35届中国控制会议

作者： Hao Meng Xiaopeng He Jingguo Song Zhilin Liu College of Automation Harbin Engineering University

ISBN: (纸本)9781509009107

ECDIS(Electronic Chart Display and Information System) is a real-time navigation system,which integrates the chart display,navigation operations,route calculation and navigation marks,*** provides a great convenience for maritime *** planning in ECDIS system plays a significant *** on the principle of intelligent ant colony algorithm,and in view of the ant colony algorithm is slow convergence speed,easy to fall into local optimal solution,the article uses adaptive pseudo random selection rules,dynamically adjusts volatilization factor p,updates the optimal route of ants pheromone,so as to improve the convergence speed of path planning and the effect of algorithm to achieve the goal of the optimal *** results validate the feasibility of the improved ant colony algorithm and its effectiveness.

关键词： -ECDIS ant colony optimization algorithm Path planning Optimal route

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An ant colony Optimizing algorithm Based on Scheduling Preference for Maximizing Working Time of WSN 15

An Ant Colony Optimizing Algorithm Based on Scheduling Prefe...

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Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

作者： Yu Liu Wei-Neng Chen Xiao-min Hu Jun Zhang Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781450334723

With the proliferation of wireless sensor networks (WSN), the issues about how to schedule all the sensors in order to maximize the system's working time have been in the spotlight. Inspired by the promising performance of ant colony optimization (ACO) in solving combinational optimization problem, we attempt to apply it in prolonging the life time of WSN. In this paper, we propose an improved version of ACO algorithm to get solutions about selecting exact sensors to accomplish the covering task in a reasonable way to preserve more energy to maintain longer active time. The methodology is based on maximizing the disjoint subsets of sensors, in other words, in every time interval, choosing which sensor to sustain active state must be rational in certain extent. With the aid of pheromone and heuristic information, a better solution can be constructed in which pheromone denotes the previous scheduling experience, while heuristic information reflects the desirable device assignment. Orderly sensor selection is designed to construct an advisable subset for coverage task. The proposed method has been successfully applied in solving limited energy assignment problem no matter in homogenous or heterogeneous WSNs. Simulation experiments have shown it has a good performance in addressing relevant issues.

关键词： schedule wireless sensors network (WSM) ant colony optimization algorithm maximize working time

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A Load Balancing algorithm for 5G Vehicular Cloud Computing Systems 2020

A Load Balancing Algorithm for 5G Vehicular Cloud Computing ...

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24th Pan-Hellenic Conference on Informatics

作者： Eirini Zoumi Emmanouil Skondras David Veliu Angelos Michalas Dimitrios D. Vergados University of Piraeus Greece University of Western Macedonia Greece

ISBN: (纸本)9781450388979

5G Vehicular Cloud Computing (5G-VCC) infrastructures are evolving rapidly. In a 5G-VCC system, Cloud resources should be efficiently distributed to provide satisfactory Quality of Service (QoS) for modern services with increased requirements. Furthermore, the workload should be fairly distributed to the available Virtual Machines (VMs). This paper proposes an algorithm for performing load balancing in Cloud infrastructures that exist in 5G-VCC systems. The algorithm is called Modified ant colony optimization (MACO), as its functionality is influenced by the natural behaviour of ants. Specifically, the MACO algorithm assigns a pheromone (weight) value to each VM. Subsequently, for each service request the VM with the highest pheromone is selected in a way similar to the one that ants apply to select optimal routes. The selection of each VM results in the decrement its pheromone value, considering the workload of the assigned service. Evaluation results show that the proposed algorithm outperforms existing load balancing algorithms in terms of the processing time required for serving the user requests.

关键词： 5G-VCC systems cloud services ant colony optimization algorithm load balancing

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Study on Automatic Relaying algorithm for PLC Based on Channel State

Study on Automatic Relaying Algorithm for PLC Based on Chann...

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2010 Second International Conference on Communication Systems, Networks and Applications (ICCSNA 2010)

作者： Wenbing Lu Department of Electrical and Electronic Engineering North China Electric Power University Beijing 102206,China Yingli Luo Department of Electrical and Electronic Engineering North China Electric Power University Beijing 102206,China Xiaowei Bi Department of Electrical and Electronic Engineering North China Electric Power University Beijing 102206,China Yonghong Ma Department of Electrical and Electronic Engineering North China Electric Power University Beijing 102206,China Yongquan Chen Department of Electrical and Electronic Engineering North China Electric Power University Beijing 102206,China

Relaying communication is a necessary means to improve the reliability of Meter Reading System(MRS) based on Power Line Carrier(PLC),but the searching for relaying path is one of the biggest *** to the characteristics of PLC networks,an adaptive automatic relaying method based on the parameters of power line channel state and improved ant colony optimization algorithm is put forward in this *** the proposed algorithm,the restricted candidate list(RCL) strategy utilizing the greed stochastic adaptive searching method is also introduced according to the parameters of *** simulation and experimental results indicate that the algorithm can be adaptive to the channel state's change by updating routing table dynamically,effectively improve the accuracy of relay and the success rate,and improve the search efficiency.

关键词： PLC meter-reading system amplitude parameter ant colony optimization algorithm automatic relaying algorithm restricted candidate list(RCL)

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