检索结果-内蒙古大学图书馆

3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Cleetus, Nimmy Dhanya, K. A. SCMS Sch Engn & Technol Dept Comp Sci & Engn Ernakulam Kerala India

ISBN: (纸本)9781479930807

The paper constitutes the study of particle swarm optimization in multi-objective functions. Swarm intelligence plays a vital role in intrusion detection. Intrusion detection system identifies the normal as well as abnormal behavior of a system. The weighted aggregation method is considered as multi-objective functions. We propose an intrusion detection mechanism based on particle swarm optimization which has a strong global search capability and used for the dimensionality optimization. Random forest is used as the classifier for modelling attacks and legitimate set. An accuracy of 91.71% at detection time of 0.22sec is obtained.

关键词： Particle Swarm Optimization multi-objective functions intrusion detection fitness function

来源：评论

学校读者我要写书评

暂无评论

Optimal operation of the smart electrical network considering energy management of demand side

SCIENCE AND TECHNOLOGY FOR ENERGY TRANSITION

引用

SCIENCE AND TECHNOLOGY FOR ENERGY TRANSITION 2025年第1期80卷

作者： Askar, Shavan Saidoune, Fatmazohra Abduvalieva, Dilsora Ezhilarasan, G. Thakur, Rishabh Yadav, Yashpal Kumar, Raj Hussen, Mohammed Jawad, Israa Abed Ali, A. M. Erbil Polytech Univ Erbil Tech Engn Coll Informat Syst Engn Dept Erbil Iraq Univ Medea Renewable Energy & Mat Lab LERM Medea 26000 Algeria Tashkent State Pedag Univ Dept Math & Informat Technol Bunyodkor Ave 27 Tashkent 100070 Uzbekistan JAIN Sch Engn & Technol Dept Elect & Elect Engn Bangalore Karnataka India Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India NIMS Univ Rajasthan NIMS Inst Engn & Technol Dept Elect Elect & Elect Vehicle Engn Jaipur Rajasthan India Chandigarh Grp Coll Jhanjeri Chandigarh Engn Coll Dept Elect & Commun Engn Mohali 140307 Punjab India Islamic Univ Coll Tech Engn Dept Comp Tech Engn Najaf Iraq Islamic Univ Al Diwaniyah Coll Tech Engn Dept Comp Tech Engn Al Diwaniyah Iraq Islamic Univ Babylon Coll Tech Engn Dept Comp Tech Engn Babylon Iraq Al Nisour Univ Coll Dept Technol Baghdad Iraq Ajman Univ Energy Syst Dept Ajman U Arab Emirates

The smart electrical grid represents a significant advancement in generating, distributing, and consuming electricity. This sophisticated system integrates modern technology and communication tools to enhance energy management efficiency and improve demand costuming within the power network. In this paper, optimal operation of the electrical network with energy management and Demand Response Program (DRP) is implemented. The implementation of the optimal operation is done via multi-stage and multi-objective functions modeling. The DRP modeling is done in first stage to optimal management of consumption in demand side. In second stage, operating cost, emission, power losses and voltage profile are optimized as multi-objective functions modeling with attention to optimal management of consumption in demand side. The solving optimal operation of the electrical network is carried out by using Elephant Herding Optimization (EHO). This problem is implemented on 33-bus test system with hybrid energy resources. Finally, DRP leads to reducing costs, emissions and losses and improving voltage profile in proposed electrical network. Hence, operation costs, emission, power losses, and voltage deviation with the participation of DRP are minimized by 39.15%, 9.94%, 33.35%, and 30.73%, respectively. On the other side, voltage stability is enhanced by 3.66% without considering DRP.

关键词： Smart electrical grid Energy management Demand response program multi-objective functions Optimal operation

来源：评论

学校读者我要写书评

暂无评论

PYGENALGO: A simple and powerful toolkit for genetic algorithms

引用

SOFTWAREX 2025年 30卷

作者： Vrettas, Michail D. Silvestri, Stefano Natl Res Council ICAR CNR Inst High Performance Comp & Networking Via Pietro Castellino 111 I-80131 Naples Italy

Genetic algorithms (GAs) are meta-heuristic algorithms that are used for solving constrained and unconstrained optimization problems, mimicking the process of natural selection in biological evolution. Due to the fact that GAs do not require the optimization function to be differentiable, they are suitable for application in cases where the derivative of the objective function is either unavailable or impractical to obtain numerically. This paper proposes a general purpose genetic algorithm toolkit, implemented in Python3 programming language, having only minimum dependencies in NumPy and Joblib, that handle some of the numerical and parallel execution details.

关键词： Genetic algorithms Optimization Evolutionary algorithms Island model (parallel) multi-objective functions Python3

来源：评论

学校读者我要写书评

暂无评论

multiobjective Fitness functions With Nonlinear Switching for Antenna Optimizations

IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION

引用

IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION 2022年 3卷 613-626页

作者： Khan, Md Rayhan Zekios, Constantinos L. Bhardwaj, Shubhendu Georgakopoulos, Stavros, V Florida Int Univ Dept Elect & Comp Engn Miami FL 33199 USA

In a multi-objective optimization process, several goals are traditionally combined into a single fitness function. In such cases, the choice of the objective function is critical, as it should accurately represent the desired optimization goals. Here, we introduce a new class of multi-objective functions with non-linearity and switching behavior, and also provide a method for objective function engineering. Notably, the proposed objective functions introduce versatile forms of fitness growth during the optimization, and provide a systematic approach for integrating the expertise in antenna design with the optimization process. The proposed optimization processes are applied in antenna optimization to demonstrate their enhanced performance. Our optimization examples consider problems based on both analytical electromagnetic models and full-wave simulation. Specifically, we consider the designs of an end-fire array, a pyramidal horn antenna, a Yagi-Uda array, and a wideband patch antenna. Our results suggest that, with minimum computation effort, the proposed non-linear fitness functions produce better performing designs when compared to a linear summation-based fitness function, e.g., 12% higher forward gain for the Yagi-Uda array, 9.3% lower side lobe level for the horn antenna, 23.38% higher directivity for the end-fire array, and approximately 1.5 times higher bandwidth for the wideband patch antenna.

关键词： Optimization Linear programming Switches Antenna arrays Logistics Horn antennas Broadband antennas Antenna optimization cost function multi-objective functions non-linear function particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

A comparative study of different objective functions to improve the flood forecasting accuracy

A comparative study of different objective functions to impr...

引用

28th Nordic Hydrological Conference

作者： Jie, Meng-Xuan Chen, Hua Xu, Chong-Yu Zeng, Qiang Tao, Xin-e Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China Univ Oslo Dept Geosci Oslo Norway

In the calibration of flood forecasting models, different objective functions and their combinations could lead to different simulation results and affect the flood forecast accuracy. In this paper, the Xinanjiang model was chosen as the flood forecasting model and shuffled complex evolution (SCE-UA) algorithm was used to calibrate the model. The performance of different objective functions and their combinations by using the aggregated distance measure in calibrating flood forecasting models was assessed and compared. And the impact of different thresholds of the peak flow in the objective functions was discussed and assessed. Finally, a projection pursuit method was proposed to composite the four evaluation indexes to assess the performance of the flood forecasting model. The results showed that no single objective function could represent all the characteristics of the shape of the hydrograph simultaneously and significant trade-offs existed among different objective functions. The results of different thresholds of peak flow indicated that larger thresholds of peak flow result in good performance of peak flow at the expense of bad simulation in other aspects of hydrograph. The evaluation results of the projection pursuit method verified that it can be a potential choice to synthesize the performance of the multiple evaluation indexes.

关键词： flood forecasting multi-objective functions projection pursuit single objective function threshold of peak flow

来源：评论

学校读者我要写书评

暂无评论

multi-objective forest harvesting under sustainable and economic principles

引用

Journal of Forestry Research 2023年第5期34卷 1379-1394页

作者： Talles Hudson Souza Lacerda Luciano Cavalcante de Jesus Franca Isáira Leite e Lopes Sammilly Lorrayne Souza Lacerda Evandro OrfanóFigueiredo Bruno Henrique Groenner Barbosa Carolina Souza Jarochinski e Silva Lucas Rezende Gomide Suzano SA Sao JoséDos CamposSao PauloBrazil Institute of Agricultural Sciences Federal University of Uberlandia–UFUMonte CarmeloMinas GeraisBrazil Department of Forest Sciences Federal University of Lavras–UFLALavrasMinas GeraisBrazil Brazilian Agricultural Research Corporation–EMBRAPA Rio BrancoAcreBrazil Department of Systems and Automation Federal University of Lavras–UFLALavrasMinas GeraisBrazil

Selective logging is well-recognized as an effective practice in sustainable forest ***,the ecological efficiency or resilience of the residual stand is often in *** time depends on operational variables,diversity,and forest *** logging is excellent but is open to *** may be resolved by mathematical programming and this study integrates the economic-ecological aspects in multi-objective function by applying two evolutionary *** function maximizes remaining stand diversity,merchantable logs,and the inverse of distance between trees for harvesting and log landings *** Brazilian rainforest database(566 trees)was used to simulate our 216-ha *** log landing design has a maximum volume limit of 500 *** nondominated sorting genetic algorithm was applied to solve the main optimization *** parallel,a sub-problem(p-facility allocation)was solved for landing allocation by a genetic *** frontier analysis was applied to distinguish the gradientsα-economic,β-ecological,andγ-*** expected,the solutions have high diameter changes in the residual stand(average removal of approximately 16 m^(3) ha^(-1)).All solutions showed a grouping of trees selected for harvesting,although there was no formation of large clearings(percentage of canopy removal<7%,with an average of 2.5 ind ha^(-1)).There were no differences in floristic composition by preferentially selecting species with greater frequency in the initial stand for *** implies a lower impact on the demographic rates of the remaining *** methodology should support projects of reduced impact logging by using spatial-diversity information to guide better practices in tropical forests.

关键词： Amazon rainforest management Computational intelligence multi-objective functions Evolutionary computing

来源：评论

学校读者我要写书评

暂无评论

multi-objective Meta-Heuristic Approach for Energy-Efficient Secure Data Aggregation in Wireless Sensor Networks

引用

WIRELESS PERSONAL COMMUNICATIONS 2015年第1期81卷 1-16页

作者： Krishna, M. Bala Doja, M. N. GGS Indraprastha Univ Univ Sch Informat & Commun Technol New Delhi India JMI Cent Univ Dept Comp Engn Fac Engn & Technol New Delhi India

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.

关键词： Energy-efficient clusters Secure data aggregation Energy-efficient secure data aggregation multi-objective functions multi-objective optimization Meta-heuristics

来源：评论

学校读者我要写书评

暂无评论

Y-stiffened panel multi-objective optimization using genetic algorithm

引用

THIN-WALLED STRUCTURES 2009年第11期47卷 1331-1342页

作者： Badran, Sherif Farouk Nassef, Ashraf O. Metwalli, Sayed M. Arab Acad Sci & Technol Marine Engn Technol Dept Alexandria 21937 Egypt Maritime Transportat Alexandria 21937 Egypt Amer Univ Cairo Fac Engn Mech Design & Prod Dept Cairo 11511 Egypt Cairo Univ Fac Engn Mech Design & Prod Dept Giza 12316 Egypt

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 objective functions 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.

关键词： Buckling Y-stiffened panel Genetic algorithm multi-objective functions

来源：评论

学校读者我要写书评

暂无评论

Optimal hybrid participation of customers in a smart micro-grid based on day-ahead electrical market

引用

ARTIFICIAL INTELLIGENCE REVIEW 2022年第7期55卷 5891-5915页

作者： Chamandoust, Heydar Islamic Azad Univ Dept Elect Engn Damavand Branch Tehran Iran

One of main challenges in the many countries with attention to growth people in the world is sustainable consumption and production of energy to improve environmental and economic issues by smart energy systems. In this paper, a multi-objective function model is developed to supply the demand of a smart micro-grid (SMG) with the aim of minimizing first) the operation cost, second) the emission pollution, and third) the deviation between the original demand curve and its desired level in the day-ahead time period. The third proposed objective function is a new strategy which can be used by the SMG operators to manage the demand consumption through responsible customers (RCs) with shiftable loads. Moreover, a number of consumers can participate in the energy management problem of the system through curtailing the demand as a reserve. The proposed objective functions are optimized to obtain the non-dominated solutions using the epsilon-constraint method. Then, the best solution is selected using combined fuzzy and Weighted sum approaches. To evaluate the effectiveness of the proposed model and its solution approach, it is applied on a test system considering four different case studies. The emission pollution and operation cost in the first case (base case) are 8832.24 kg and $692,930.2. In second case and with the participation of reserve, the reduction of the operation cost and the emission are equal to 6.03% and 7.98% than first case. With the participation of the demand shifting strategy in third case, operation cost and the emission are decreased by 20.2% and 19.89% according to base case. Finally, in fourth case and with participation of reserve and demand shifting strategy, the operation cost and the emission pollution are reduced by 26.5% and 38.1% in comparison with the base case.

关键词： multi-objective functions Smart micro-grid Reserve Epsilon-constraint method Weighted sum approach

来源：评论

学校读者我要写书评

暂无评论

Evolutionary multi-objective Optimization Algorithm for Resource Allocation Using Deep Neural Network in 5G multi-User Massive MIMO

引用

INTERNATIONAL JOURNAL OF ELECTRONICS 2021年第7期108卷 1214-1233页

作者： Purushothaman, K. E. Nagarajan, V. Adhiparasakthi Engn Coll Dept Elect & Commun Engn Melmaruvathur India

5G network plays a vital role in each field. Recently, the number of 5G users are increasing due to their vast merits. But, these users require various resources to operate effectively. Recently, the machine learning approaches are introduced within this 5G network field for attaining better accuracy and reliability during resource allocation. In this work, the resource allocation for multi-users in 5G massive-MIMO (mMIMO) is performed by a deep neural network (DNN). Initially, the objective functions are optimised by multi-objective Sine Cosine algorithm (MOSCA). The objective functions applied in this optimisation method are data rate, signal-interference noise ratio (SINR), power consumption, and energy efficiency (EE). Next, these optimised objective functions are allocated to the neural network for resource allocation. DNN identifies the requirement level of each user. Based on this level, it allocates the resource to each user by maintaining high throughput and EE. Further, the fairness index for this neural network-based resource allocation process is also identified. The throughput and fairness index of the proposed method for 30 users are 275 bps, and 0.92%, respectively. The outcomes of this proposed method show that this method provides better performance in 5G mMIMO than other existing methods.

关键词： 5G mMIMO multi-objective functions MOSCA energy efficiency DNN

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：