Energy consumption in the sensor network is primarily due to the switching states of radio transceivers and long busy states of sensor nodes in the network. Data aggregation techniques reduce the number of transmissio...
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Energy consumption in the sensor network is primarily due to the switching states of radio transceivers and long busy states of sensor nodes in the network. Data aggregation techniques reduce the number of transmissions and improve the bandwidth utilization. Secure data aggregation and energy-efficient routing protocols establish the secure channel, and reduce the communication overhead in the network. multi-objective optimization methods based on the weighted sum method, the utility method and meta-heuristic search methods enhance the performance of meta-heuristic algorithms. This article proposes multi-objective meta-heuristic approach for energy-efficient secure data aggregation (MH-EESDA) protocol in wireless sensor networks. The proposed protocol uses divide-and-conquer approach to form the secure clusters and perform the secure data aggregation in energy-efficient route paths of the network. The protocol functions in three phases. In the first phase, the clusters are formed, in the second phase, the secure nodes are selected and in the third phase, energy-efficient data aggregation is performed across the secure route paths of the network. The sensor node energy and data aggregation rate are evaluated for (1) minimum degree of intrusions (2) threshold-based degree of intrusions and (3) maximum degree of intrusions in the network. Simulation results illustrate significant improvements in the proposed MH-EESDA protocol.
In this paper, we proposed particle swarm optimization using multi-objective functions. Intrusion detection system has a significant role in research methodology. Intrusion detection system identifies the normal as we...
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ISBN:
(纸本)9788132222088;9788132222071
In this paper, we proposed particle swarm optimization using multi-objective functions. Intrusion detection system has a significant role in research methodology. Intrusion detection system identifies the normal as well as abnormal behavior of a system. Swarm intelligence plays an essential role in intrusion detection. Random forest classifier is used for detecting attacks. Intrusion detection mechanism based on particle swarm optimization which has a strong global search capability is used for dimensionality optimization. Weighted aggregation method is employed as multi-objective functions. The proposed system has the high intrusion detection accuracy of 97.54 % with a detection time is 0.20 s.
The aim of this paper is to design an optimum Y-stiffener plate combination using multi-objective optimization with real-coded genetic algorithms under the action of uniaxial compressive loads, because the most import...
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The aim of this paper is to design an optimum Y-stiffener plate combination using multi-objective optimization with real-coded genetic algorithms under the action of uniaxial compressive loads, because the most important loads applied on stiffened plates in ship hull is longitudinal in-plane axial compression arising for instance due to longitudinal bending because the cargo is not distributed equally in holds or due to grounding, stranding or collision. Five of the Y-stiffened panel dimensions were selected to be the independent design variables of the optimization problem. The objectivefunctions are the ultimate buckling load and the volume per unit area of the Y-stiffener plate combination. Nonlinear finite element analysis was used to calculate the ultimate buckling load of 35 different sets of the design variables, with certain range for each of the design variables. The effects of independent design variables on the ultimate buckling load and the volume per unit area for Y-stiffener plate combination were studied and discussed. A new surrogate function to predict the ultimate buckling load of Y-stiffener plate combination is created and validated using the values of the ultimate buckling loads calculated using nonlinear finite element analysis. The proposed surrogate function is valid only in the specific ranges of the design variables. The Pareto optimal sets were calculated using multi-objective optimization with real-coded genetic algorithms and the optimum set of the independent design variables which is associated with the optimal geometric dimensions of the Y-stiffened panel was selected as the set which has the maximum ultimate buckling load to volume per unit area ratio. The optimum set was tested and validated using sensitivity analysis technique. (C) 2009 Elsevier Ltd. All rights reserved.
An ideal multi-objective financial decision support (MFDS) model for contractors should have ability of handling both the quantitative and qualitative inputs. This paper aims to develop a new MFDS model, which attempt...
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An ideal multi-objective financial decision support (MFDS) model for contractors should have ability of handling both the quantitative and qualitative inputs. This paper aims to develop a new MFDS model, which attempts to provide a more accurate way to support financial decision-makers in Chinese state-owned construction firms. Owing to the slow progress of passing the construction regulations, coupled with the inherited ownership problems in the Chinese booming construction industry, it is logical for Chinese project managers to make financial decisions with the consideration of the four objectivefunctions (profit margin, risk factors, government relationship and market share), which are realistic representations of the present situation of Chinese construction industry. The proposed MFDS model is a practical method to support financial decision-making. For the model's computational performance, Module 3 (stochastic universal sampling selection, adaptive crossover and adaptive Mutation of Genetic Algorithm) is more superior. (c) 2008 Elsevier B.V. All rights reserved.
A large number of logistic problems for Supply Chain Management are discussed in recent production environment. Most logistic problems are resolved as problems involving a single objective function such as total trave...
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ISBN:
(纸本)9781846260032
A large number of logistic problems for Supply Chain Management are discussed in recent production environment. Most logistic problems are resolved as problems involving a single objective function such as total traveled distance, cost, and so on. However, different multiple objectivefunctions are required in some real problems and their functions have trade-off relationship. Then, several objectivefunctions are required to evaluate simultaneously and to search for Pareto solutions. This study discusses a vehicle routing problem in which different objectivefunctions are evaluated simultaneously. And, hybrid type of genetic algorithm is proposed to resolve multi-objective vehicle routing problem. When different methods are adaptive to search for different single objectivefunctions in the multi-objective problem, Pareto-front sought by either method tends to incline toward the axis of the objective function to which the method is more adaptive. In order to search for approximate optimal Pareto-front, the different methods which are adoptive for different single objectivefunctions are incorporated into multi-objective genetic algorithm as the proposed method. The proposed method is examined on a bench-mark problem to evaluate its performance. The result shows that the proposed method obtains approximate Pareto solutions distributed uniformly in a coordinate system of objectivefunctions in a practical computational time.
In a changeover design, each subject receives a sequence of treatments over consecutive time periods. We provide simple recursive formulae for updating the average efficiency factors of both direct and residual treatm...
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In a changeover design, each subject receives a sequence of treatments over consecutive time periods. We provide simple recursive formulae for updating the average efficiency factors of both direct and residual treatment effects, after two treatments are interchanged. We have incorporated these formulae into two interchange algorithms. From a comparison with designs generated from other algorithms using surrogate objectivefunctions, we show that these recursive methods generally produce more efficient designs more quickly. (C) 2002 Elsevier Science B.V. All rights reserved.
According to the characteristics of distribution systems, this paper proposes a real-time algorithm for solving the optimal control strategies of reactive power and voltage in distribution systems. Under the condition...
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According to the characteristics of distribution systems, this paper proposes a real-time algorithm for solving the optimal control strategies of reactive power and voltage in distribution systems. Under the conditions of bus voltage, branch reactive power used as controlled variables and the setting of the Tap Changing Under Load Transformers, reactive power compensation used as control variables, the strategies are gotten by using Linear Programming in accordance with the requirements of real-time control. There are several different objectivefunctions in the paper. That is: (1).minimum real power losses; (2) minimum reactive power compensation; (3).maximum control gain. The objectivefunctions mentioned above can be choosed in the light of specific circumstance. The paper can follow the sample date in the real-time control, simultaneously, identify the reactive power-voltage static characteristic on load bus, and consider its influence upon the optimal control strategies. The proposed technique has been tested on the IEEE 5-bus, 6-bus, modified 30-bus and Jiaxing distribution system(41-bus) in China, on the personal conputer. The optimal control strategies for different objective function mentioned above are obtained rapidly.
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