检索结果-内蒙古大学图书馆

Enhanced fuzzy C-means clustering based cooperative spectrum sensing combined with multi-objective resource allocation approach for delay-aware CRNs

引用

IET COMMUNICATIONS 2020年第4期14卷 619-626页

作者： Sonti, Siva Reddy Prasad, Mutchakayala Siva Ganga KLEF Dept Elect & Commun Engn Vijayawada Andhra Pradesh India

In cognitive radio networks (CRNs), the resource allocation is viewed as a multi-objective optimisation problem in terms of limitation of quality of service, the capacity of a channel and the transmitted power. To overcome these individual issues many researchers have been undertaken, but it does not solve the multi-objective problems. In this study, the authors propose a multi-objective random walk grey wolf optimisation (MORWGWO) algorithm to enhance the resource allocation in the CRNs. Here, they combined the spectrum sensing process with the resource allocation process. Initially, an enhanced fuzzy C-means algorithm based cluster formation is proposed for spectrum sensing and then they model the resource allocation process and propose a MORWGWO algorithm for CRNs which generates the Pareto front inbetween of different objectives in a time-efficient manner. The simulation result shows that the proposed method significantly enhances the network performance in terms of delay, delivery ratio, throughput, network lifetime, energy consumption, and fairness index. Result shows that the proposed method has a better throughput of 23.22, 15.09, 6.13% for varying nodes and 27.25, 10.62, 7.09% for varying data transfer rates while comparing with the multiple objective particle swarm optimisation, non-dominated sorting genetic algorithm, and multi-objective evolutionary algorithm.

关键词： pattern clustering cognitive radio Pareto optimisation genetic algorithms particle swarm optimisation signal detection resource allocation radio spectrum management cooperative communication power consumption quality of service multiobjective optimisation problem spectrum sensing process MORWGWO algorithm multiple objective particle swarm optimisation nondominated sorting genetic algorithm multiobjective evolutionary algorithm multiobjective resource allocation cognitive radio networks delay-aware CRN multiobjective random walk grey wolf optimisation enhanced fuzzy C-means clustering ooperative spectrum sensing quality of service transmitted power Pareto front network lifetime energy consumption fairness index

来源：评论

学校读者我要写书评

暂无评论

Calibration of imprecise and inaccurate numerical models considering fidelity and robustness: a multi-objective optimization-based approach

引用

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2015年第3期51卷 659-671页

作者： Atamturktur, Sez Liu, Zhifeng Cogan, Scott Juang, Hsein Clemson Univ Glenn Dept Civil Engn Clemson SC 29634 USA Univ Franche Comte Dept Appl Mech F-25030 Besancon France

Traditionally, model calibration is formulated as a single objective problem, where fidelity to measurements is maximized by adjusting model parameters. In such a formulation however, the model with best fidelity merely represents an optimum compromise between various forms of errors and uncertainties and thus, multiple calibrated models can be found to demonstrate comparable fidelity producing non-unique solutions. To alleviate this problem, the authors formulate model calibration as a multi-objective problem with two distinct objectives: fidelity and robustness. Herein, robustness is defined as the maximum allowable uncertainty in calibrating model parameters with which the model continues to yield acceptable agreement with measurements. The proposed approach is demonstrated through the calibration of a finite element model of a steel moment resisting frame.

关键词： nondominated sorting genetic algorithm Info-gap decision theory Info-gap uncertainty model Prediction looseness Experiment-based model validation Self-consistency

来源：评论

学校读者我要写书评

暂无评论

The best currently known class of dynamically equivalent cellular automata rules for density classification

引用

NEUROCOMPUTING 2006年第1-3期70卷 35-43页

作者： de Oliveira, Pedro P. B. Bortot, Jose C. Oliveira, Gina M. B. Univ Presbiteriana Mackenzie Fac Computacao & Informat BR-01302907 Sao Paulo SP Brazil Fundacao Armando Alvares Penteado Fac Computacao & Informat BR-01242902 Sao Paulo SP Brazil Univ Fed Uberlandia Fac Computacao BR-38400902 Uberlandia MG Brazil

The possibility of performing computations with cellular automata (CAs) opens up new conceptual issues in emergent computation. Driven by this motivation, a recurring problem in this context is the automatic search for good one-dimensional, binary CA rules that can perform well in the density classification task (DCT), that is, the ability to discover which cell state outnumbers the other state. In the past, the most successful attempts to reach this target have relied on evolutionary searches in the space of possible rules. Along this line, a multiobjective, heuristic evolutionary approach, implemented as a distributed cooperative system, is presented here, which yielded outstanding results, including a rule that led to the characterisation of a class of four equivalent rules, all of them with the best performance currently available in the literature for the DCT. (c) 2006 Elsevier B.V. All rights reserved.

关键词： cellular automata evolutionary multiobjective optimisation density classification task nondominated sorting genetic algorithm dynamic behaviour emergent computation

来源：评论

学校读者我要写书评

暂无评论

Impact of Optical Power in the Guard-Band Reduction of an Optimized DDO-OFDM System

引用

JOURNAL OF LIGHTWAVE TECHNOLOGY 2015年第23期33卷 4717-4725页

作者： Pereira, Esequiel da V. Rocha, Helder R. de O. Nunes, Reginaldo B. Segatto, Marcelo E. V. Silva, Jair A. L. Univ Fed Espirito Santo Dept Elect Engn BR-29043900 Vitoria Espirito Santo Brazil Univ Fed Espirito Santo Dept Comp & Elect BR-29930000 Sao Mateus Espirito Santo Brazil

A multiobjective optimization on the parameters of direct-detection optical orthogonal frequency division multiplexing (DDO-OFDM) systems in short-range links is proposed. Based on genetic algorithms, the optimization process takes into account the influence of optical power in the guard-band reduction of such multicarrier systems with 4- and 16-quadratic-amplitude modulation (QAM) subcarrier mapping and for propagation in 40 km of standard single-mode fiber (SSMF). Simulation results show that the parameters optimization is responsible for a gain of approximately 2.5 dB [approximate to 1.8 dB] in the required optical power for bit-error-rate <= 10(-3), for DDO-OFDM system transmission over 40 km of uncompensated SSMF, with a guard band B-G equals to 50% of the signal bandwidth B-w and 16-QAM [4-QAM] subcarrier mapping. For an extremely reduced guard band of only 1% of B-w, the gain reduced to approximate to 0.5 dB and approximate to 1.5 dB for 16- and 4-QAM mapping, respectively. The optimization process is experimentally validated under the transmission of the optimized DDO-OFDM systems in optical back-to-back (B2B) configuration and 40 km of uncompensated SSMF. A fair performance comparison is discussed in the experimental results considering B-G/B-w = 0.5 and B-G/B-w = 0.01 in the range of the optimized optical powers. It is shown that a maximum optical power penalty of approximately 2 dBm is registered according to a guard-band reduction from 50% to 1% of the useful bandwidth, for transmission over the 40 km of SSMF, considering 16-QAM subcarrier mapping.

关键词： Direct-detection orthogonal frequency division multiplexing multi-objective optimization nondominated sorting genetic algorithm spectral efficiency

来源：评论

学校读者我要写书评

暂无评论

Optimization of RNN-LSTM Model Using NSGA-II algorithm for IOT-based Fire Detection Framework

引用

IETE JOURNAL OF RESEARCH 2024年第7期70卷 6239-6254页

作者： Roy, Tanushree Shome, Saikat kumar Govt India Cent Mech Engn Res Inst Minist Sci & Technol CSIR Durgapur 713209 India

The occurrence of fire leads to unparalleled loss of resources as well as human life and hence, fire detection systems must be trustworthy and less erroneous. Real-time assessment of fire conditions through predictive learning models could lead to easier decision-making and timely rescue operations. Reported works are often restricted to the use of singular rule-based algorithms which can hardly offer a comprehensive solution by adapting to changing dynamics of fire conditions due to their static features, mostly leading to inaccurate classification. In this research, an efficient fire prediction framework has been proposed by efficiently combining the outputs of recurrent neural networks which bear the advantages of short-term predictions with long short-term memory that takes care of long-term predictions. Weights to combine multi-objective optimization functions to minimize mean absolute error and root mean square error have been designed using non-dominated sorting genetic algorithm II (NSGA-II) which offers a lower time complexity. The benchmark dataset from the NIST website has been chosen for analysis and performance validation of the proposed classifier on experimental data pertaining to different fire scenarios. A Pareto optimal front has been obtained from the proposed algorithm which represents the optimum solutions. The performance of the proposed model has been exhaustively evaluated through different factors such as accuracy, RMSE, MAE, F-Measure, binary classification rate, negative predictive value, recall and precision which justifies its contribution. The proposed model reduced RMSE by 14.95-19.88% compared to baseline machine learning models along with an enhanced accuracy of 95.05% and reduced false positive rate which is better compared to reported works along with improvement in F-Measure. Results show that the proposed NSGA-II-based RNN LSTM model accurately predicts the occurrence of fire events with reduced false alarms while maintaining a l

关键词： Fire alarm Long short-term memory Multi-objective evolutionary algorithm nondominated sorting genetic algorithm Recurrent neural network Time series

来源：评论

学校读者我要写书评

暂无评论

Fast optimization of multipump Raman amplifiers based on a simplified wavelength and power budget heuristic

引用

OPTICAL ENGINEERING 2015年第1期54卷 1-7页

作者： Rocha, Helder R. de O. Castellani, Carlos E. S. Silva, Jair A. L. Pontes, Maria J. Segatto, Marcelo E. V. Univ Fed Espirito Santo North Univ Ctr Espirito Santo BR-29932540 Sao Mateus ES Brazil Univ Fed Espirito Santo Telecommun Lab BR-29075910 Vitoria ES Brazil

We report a simple budget heuristic for a fast optimization of multipump Raman amplifiers based on the reallocation of the pump wavelengths and the optical powers. A set of different optical fibers are analyzed as the Raman gain medium, and a four-pump amplifier setup is optimized for each of them in order to achieve ripples close to 1 dB and gains up to 20 dB in the C band. Later, a comparison between our proposed heuristic and a multiobjective optimization based on a nondominated sorting genetic algorithm is made, highlighting the fact that our new approach can give similar solutions after at least an order of magnitude fewer iterations. The results shown in this paper can potentially pave the way for real-time optimization of multipump Raman amplifier systems. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： Raman amplifier wavelength division multiplexing system nondominated sorting genetic algorithm multiobjective optimization heuristic

来源：评论

学校读者我要写书评

暂无评论

Preventive/Corrective Security Constrained Optimal Power Flow Using a Multiobjective genetic algorithm

引用

ELECTRIC POWER COMPONENTS AND SYSTEMS 2018年第13期46卷 1462-1477页

作者： Galvani, Sadjad Talavat, Vahid Marjani, Saeed Rezaeian Urmia Univ Dept Power Engn Fac Engn Orumiyeh Iran

Intensive researches have been investigated the security aspect of optimal power flow (OPF) problem. The primary objective of them is to ensure the reliable and continuous supply to the consumers as economically as possible. Although all of these researches offer valuable solution to enhancing security constrained OPF (SCOPF), they avoided a discussion on the operation cost of power system versus the security constrains. The understanding of relationship between the operation cost and system security leads to make best decisions on tradeoffs between operating cost and system reliability. The main objective of this article is to solve preventive/corrective SCOPF (P/C-SCOPF) problem considering the expected power not served (EPNS) index as security constraint. Also, the relationship between operation cost and EPNS index is discussed in detail. In this article, well-known nondominated sorting genetic algorithm (NSGA) is used to determine the precise relationship between operating cost and the security constraint (EPNS), for the first time. This would result in illuminating information that can be utilized to make a reasonable decision on the best tradeoffs between operating cost and reliability of system. The proposed approach has been evaluated on IEEE 14 and 57 bus test systems and results have been discussed.

关键词： security constrained optimal power flow reliability expected power not served operating cost nondominated sorting genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Selective disassembly sequence planning under uncertainty using trapezoidal fuzzy numbers: A novel hybrid metaheuristic algorithm

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 128卷

作者： Zhang, Xuesong Fu, Anping Zhan, Changshu Pham, Duc Truong Zhao, Qiang Qiang, Tiangang Aljuaid, Mohammed Fu, Chenxi Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Peoples R China Harbin Inst Technol Sch Econ & Management Weihai 264200 Peoples R China Univ Birmingham Dept Mech Engn Birmingham B15 2TT England Northeast Forestry Univ Sch Civil Engn & Transportat Harbin 150040 Peoples R China King Saud Univ Coll Business Adm Dept Hlth Adm Riyadh Saudi Arabia Dalian Polytech Univ Sch Foreign Languages Dalian 116000 Peoples R China

Nowadays, recycling end-of-life (EoL) products has emerged as a vital approach to address resource scarcity. Within the recycling process, disassembly plays a pivotal role and has garnered substantial attention from researchers. Disassembly sequence planning (DSP) is a crucial method to enhance disassembly efficiency. Among the various DSP models, selective disassembly sequence planning (SDSP) has gained prominence as a means to save time and reduce costs. It empowers operators to locate specific components or materials in real time, thereby boosting efficiency and minimising resource wastage. However, a notable research gap exists in the domain of SDSP, particularly in uncertain environments. To bridge this gap and render SDSP solutions more practical for real-world disassembly operations, this study adopts trapezoidal fuzzy numbers to represent uncertain information within the disassembly process and formulates a comprehensive SDSP model. In response to the intricate challenges posed by this problem, we propose a hybrid approach termed nondominated sorting genetic algorithm-II with simulated large neighborhood search (NSGA-II-SLNS). This innovative algorithm leverages the strengths of the nondominated sorting genetic algorithm-II (NSGA-II), simulated annealing algorithm (SA), and large neighborhood search (LNS). Additionally, we introduce several novel search operators into NSGA-II-SLNS, including a crossover and mutation strategy based on chaotic mapping, as well as a local search operator founded on the SA criterion and LNS. To assess the effectiveness of the proposed algorithm and model, extensive numerical case studies are conducted in this research. The outcomes contribute to the advancement of rapid, nearly optimal SDSP strategies in the face of uncertainty and ambiguity in problem settings.

关键词： Selective disassembly sequence planning Trapezoidal fuzzy numbers Uncertain nondominated sorting genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

A New Hybrid Metaheuristic algorithm for Multiobjective Optimization Problems

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2020年第1期13卷 920-940页

作者： Farag, M. A. El-Shorbagy, M. A. Mousa, A. A. El-Desoky, I. M. Menoufia Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm Egypt Prince Sattam Bin Abdulaziz Univ Coll Sci & Humanities Al Kharj Dept Math Al Kharj 11942 Saudi Arabia Taif Univ Fac Sci Math & Stat Dept At Taif Saudi Arabia

The elitist nondominated sorting genetic algorithm (NSGA-II) is hybridized with the sine-cos le algorithm (SCA) in this paper to solve multiobjective optimization problems. The proposed hybrid algorithm is named nondominated sorting sine-cosine genetic algorithm (NS-SCGA). The main idea of this algorithm is the following: NS-SCGA integrates the merits of exploitation capability of NSGA-II and exploration capability of SCA for a better search ability and speeds up the searching process. 'Elie performance of NS-SCGA is tested on the set of benchmark functions provided for CEC09. The NS-SCGA results are compared with other recently developed multiobjective algorithms in terms of convergence, spacing, and spread of the obtained nondominated solutions to the true Pareto front. The statistical analysis of the results obtained is performed by nonparametric Friedman and Wilcoxon signed-rank tests. The results prove that NS-SCGA is superior to or competitive with other multiobjective optimization algorithms considered in the comparison. Furthermore, the economic emission dispatch problem (EEDP) is solved by NS-SCGA. operating cost (fuel cost) and pollutant emission of the standard IEEE 30-bus network with six generating units are minimized simultaneously by the NS-SCGA considering the losses. The results show the superiority of NS-SCGA and confirm its ability in solving EEDE TOESIS technique is applied to choose the best compromise solution from the obtained Pareto-optimal solutions of EEDP according to the decision maker's preference. (C) 2020 The Authors. Publishedby Atlantis Press SARL.

关键词： Sine-cosine algorithm nondominated sorting genetic algorithm Multiobjective optimization problems The economic emission dispatch problem TOPSIS

来源：评论

学校读者我要写书评

暂无评论

Multiobjective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power systems with renewables

引用

IET RENEWABLE POWER GENERATION 2010年第5期4卷 404-419页

作者： Katsigiannis, Y. A. Georgilakis, P. S. Karapidakis, E. S. Tech Univ Crete Dept Prod Engn & Management GR-73100 Khania Greece Natl Tech Univ Athens Sch Elect & Comp Engn GR-15780 Athens Greece Technol Educ Inst Crete Dept Environm & Nat Resources GR-73133 Khania Greece

The overall evaluation of small autonomous hybrid power systems (SAHPS) that contain renewable and conventional power sources depends on economic and environmental criteria, which are often conflicting objectives. The solution of this problem belongs to the field of non-linear combinatorial multiobjective optimisation. In a multiobjective optimisation problem, the target is not to find an optimal solution, but a set of non-dominated solutions called Pareto-set. The present article considers as an economic objective the minimisation of system's cost of energy (COE), whereas the environmental objective is the minimisation of the total greenhouse gas (GHG) emissions of the system during its lifetime. The main novelty of this article is that the calculation of GHG emissions is based on life cycle analysis (LCA) of each system's component. In LCA, the whole life cycle emissions of a component are taken into account, from raw materials extraction to final disposal/recycling. This article adopts the non-dominated sorting genetic algorithm (NSGA-II), which in combination with a proposed local search procedure effectively solves the multiobjective optimisation problem of SAHPS. Two main categories of SAHPS are examined with different energy storage: lead-acid batteries and hydrogen storage. The results indicate the superiority of batteries under both economic and environmental criteria.

关键词： Secondary cells secondary cells small autonomous hybrid power systems Pareto set optimisation power sources GHG emissions disposal-recycling Pollution detection and control Optimisation techniques nonlinear programming sorting total greenhouse gas emissions raw material extraction nondominated sorting genetic algorithm Combinatorial mathematics Other energy storage hydrogen storage multiobjective genetic algorithm Power system management, operation and economics optimum economic criteria air pollution control hybrid power systems nonlinear combinatorial multiobjective optimisation problem lead-acid batteries system cost of energy minimisation life cycle analysis environmental criteria power system economics genetic algorithms energy storage

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：