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 ...
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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 antcolonyoptimization (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.
In order to find an effective method of solving the problem of subjectivity and difficulty in the high-dimension data clustering, a new method-an improved Projection Pursuit based on ant colony optimization algorithm ...
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ISBN:
(纸本)9783642163968
In order to find an effective method of solving the problem of subjectivity and difficulty in the high-dimension data clustering, a new method-an improved Projection Pursuit based on ant colony optimization algorithm was introduced. The ant colony optimization algorithm was employed to optimize the function of the projected indexes in the PP. The ant colony optimization algorithm has the strong global optimization ability and the PP method is a powerful technique for extracting statistically significant features from high-dimension data for automatic target detection and classification. Application results show that the method can complete the selection more objectivity and rationality with objective weight, high resolving power, and stable result. The study provides a novel algorithm for the high-dimension data clustering.
In this work we present a study on the application of bio-inspired strategies for optimization to Fault Diagnosis in industrial systems. The principal aim is to establish a basis for the development of new and viable ...
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ISBN:
(纸本)9781424481262
In this work we present a study on the application of bio-inspired strategies for optimization to Fault Diagnosis in industrial systems. The principal aim is to establish a basis for the development of new and viable model-based Fault Diagnosis Methods which improve some difficulties that the current methods cannot avoid. These difficulties are related with fault sensitivity and robustness to external disturbances. To get start the study, we consider the Differential Evolution and the ant colony optimization algorithms. This application is illustrated using simulation data of the Two Tanks System benchmark. In order to analyze the merits of these algorithms to obtain a diagnosis which needs to be sensitive to faults and robust to external disturbances, some experiments with incipient faults and noisy data have been simulated. The results indicate that the proposed approach, basically the combination of the two algorithms, characterizes a promising methodology for Fault Diagnosis.
Hybrid intelligent schemes have proven their efficiency in solving NP-hard optimization problems. Portfolio optimization refers to the problem of finding the optimal combination of assets and their corresponding weigh...
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ISBN:
(纸本)9783642128417
Hybrid intelligent schemes have proven their efficiency in solving NP-hard optimization problems. Portfolio optimization refers to the problem of finding the optimal combination of assets and their corresponding weights which satisfies a specific investment goal and various constraints. In this study, a hybrid intelligent metaheuristic, which combines the ant colony optimization algorithm and the Firefly algorithm, is proposed in tackling a complex formulation of the portfolio management problem. The objective function under consideration is the maximization of a financial ratio which combines factors of risk and return. At the same time, a hard constraint, which refers to the tracking ability of the constructed portfolio towards a benchmark stock index, is imposed. The aim of this computational study is twofold. Firstly, the efficiency of the hybrid scheme is highlighted. Secondly, comparison results between alternative mechanisms, which are incorporated in the main function of the hybrid scheme, are presented.
The stochastic loader problem and the procedure for solutions were proposed in this paper. On the basis of basic ant colony optimization algorithm, the new ant colony optimization algorithm with inner and outer mutati...
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The stochastic loader problem and the procedure for solutions were proposed in this paper. On the basis of basic ant colony optimization algorithm, the new ant colony optimization algorithm with inner and outer mutation was designed to solve this problem. Two numerical examples were provided to illustrate the efficiency and reliability of this new algorithm.
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 stud...
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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.
The artificial neural network (ANN) method is used to study the macroscopic model of an actual water distribution *** the first time,the antcolonyoptimization (ACO) algorithm is implemented to optimize the node numb...
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The artificial neural network (ANN) method is used to study the macroscopic model of an actual water distribution *** the first time,the antcolonyoptimization (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.
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...
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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.
In this paper, a modified antcolony system (ACS) algorithm is proposed to find a shortest path based on the preference of links. Most of the shortest path search algorithms aim at finding the distance or time shortes...
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ISBN:
(纸本)9783642108433
In this paper, a modified antcolony system (ACS) algorithm is proposed to find a shortest path based on the preference of links. Most of the shortest path search algorithms aim at finding the distance or time shortest paths. However, these shortest paths are not surely an optimum path for the drivers who prefer choosing a less short, but more reliable or flexible path. For this reason, we propose the preference-based shortest path search algorithm which uses the properties of the links of the map. The properties of the links are specified by a set of data provided by the user of the car navigation system. The proposed algorithm was implemented in C and experiments were performed upon the map that includes 64 nodes with 118 links.
Path selection is one of the fundamental problems in emergency logistics management. Two mathematical models for path selection in emergency logistics management are presented considering more actual factors in time o...
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Path selection is one of the fundamental problems in emergency logistics management. Two mathematical models for path selection in emergency logistics management are presented considering more actual factors in time of disaster. First a single-objective path selection model is presented taking into account that the travel speed on each arc will be affected by disaster extension. The objective of the model is to minimize total travel time along a path. The travel speed on each arc is modeled as a continuous decrease function with respect to time. A modified Dijkstra algorithm is designed to solve the model. Based oil the first model, we further consider the chaos, panic and congestions in time of disaster. A multi-objective path selection model is presented to minimize the total travel time along a path and to minimize the path complexity. The complexity of the path is modeled as the total number of arcs included in the path. An ant colony optimization algorithm is proposed to solve the model. Simulation results show the effectiveness and feasibility of the models and algorithms presented in this paper. (C) 2008 Elsevier Ltd. All rights reserved.
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