检索结果-内蒙古大学图书馆

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2022年第4期7卷 910-922页

作者： Alofi, Akram Bokhari, Mahmoud A. Bahsoon, Rami Hendley, Robert Univ Birmingham Sch Comp Sci Birmingham B15 2TT England Umm Al Qura Univ Jamoum Univ Coll Comp Sci Dept Jamoum 25372 Saudi Arabia Taibah Univ Comp Sci Dept Software Engn Res Grp Medina 42353 Saudi Arabia Univ Adelaide Sch Comp Sci Optimizat & Logist Grp Adelaide 5005 Australia

Blockchain technology has gained recognition in industrial, financial, and various technological domains for its potential in decentralizing trust in peer-to-peer systems. A core component of blockchain technology is a consensus algorithm, most commonly Proof of Work (PoW). PoW is used in blockchain-based systems to establish trust among peers;however, it does require the expenditure of an enormous amount of energy that affects the environmental sustainability of blockchain-based systems. Energy minimization, whilst ensuring trust within blockchain-based systems that use PoW, is a challenging problem. The solution has to consider how energy consumption can be minimized without compromising trust, whilst still ensuring, for instance, scalability, security, and decentralization. In this paper, we represent the problem as a subset selection problem of miners in a blockchain-based system. We formulate the problem of blockchain energy consumption as a Search-Based Software Engineering problem with four objectives: energy consumption, carbon emission, decentralization, and trust. We propose a model composed of multiple fitness functions. The model can be used to explore the complex search space by selecting a subset of miners that minimizes the energy consumption without drastically impacting the primary goals of the blockchain technology (i.e., security/trustworthiness and decentralization). We integrate our proposed fitness functions into five evolutionary algorithms to solve the problem of blockchain miners selection. Our results show that the environmental sustainability of blockchain-based systems (e.g. reduced energy use) can be enhanced with little degradation in other competing objectives. We also report on the performance of the algorithms used.

关键词： Blockchain evolutionary algorithms mining optimization search-based software engineering sustainability

来源：评论

学校读者我要写书评

暂无评论

Determining factors that influence the dispersal of a pelagic species: A comparison between artificial neural networks and evolutionary algorithms

引用

ECOLOGICAL MODELLING 2011年第10期222卷 1657-1665页

作者： Pontin, D. R. Schliebs, S. Worner, S. P. Watts, M. J. Lincoln Univ Bioprotect Res Ctr Canterbury 7647 New Zealand Lincoln Univ Dept Ecol Canterbury 7647 New Zealand Auckland Univ Technol Knowledge Engn & Discovery Res Inst Auckland New Zealand Univ Adelaide Sch Earth & Environm Sci Adelaide SA Australia

Because of increasing transport and trade there is a growing threat of marine invasive species being introduced into regions where they do not presently occur. So that the impacts of such species can be mitigated, it is important to predict how individuals, particularly passive dispersers are transported and dispersed in the ocean as well as in coastal regions so that new incursions of potential invasive species are rapidly detected and origins identified. Such predictions also support strategic monitoring, containment and/or eradication programs. To determine factors influencing a passive disperser, around coastal New Zealand, data from the genus Physalia (Cnidaria: Siphonophora) were used. Oceanographic data on wave height and wind direction and records of occurrences of Physalia on swimming beaches throughout the summer season were used to create models using artificial neural networks (ANNs) and Naive Bayesian Classifier (NBC). First, however, redundant and irrelevant data were removed using feature selection of a subset of variables. Two methods for feature selection were compared, one based on the multilayer perceptron and another based on an evolutionary algorithm. The models indicated that New Zealand appears to have two independent systems driven by currents and oceanographic variables that are responsible for the redistribution of Physalia from north of New Zealand and from the Tasman Sea to their subsequent presence in coastal waters. One system is centred in the east coast of northern New Zealand and the other involves a dynamic system that encompasses four other regions on both coasts of the country. Interestingly, the models confirm, molecular data obtained from Physalia in a previous study that identified a similar distribution of systems around New Zealand coastal waters. Additionally, this study demonstrates that the modelling methods used could generate valid hypotheses from noisy and complicated data in a system about which there is little previou

关键词： Ecological informatics Artificial neural networks evolutionary algorithms Physalia Feature selection

来源：评论

学校读者我要写书评

暂无评论

A selection method for evolutionary algorithms based on the Golden Section

引用

EXPERT SYSTEMS WITH APPLICATIONS 2018年 106卷 183-196页

作者： Cuevas, Erik Enriquez, Luis Zaldivar, Daniel Perez-Cisneros, Marco Univ Guadalajara CUCEI Dept Elect Av Revoluc 1500 Guadalajara Jalisco Mexico Ctr Tapatio Educ AC Av Juarez 340 Guadalajara Jalisco Mexico

During millions of years, nature has developed patterns and processes with interesting characteristics. They have been used as inspiration for a significant number of innovative models that can be extended to solve complex engineering and mathematical problems. One of the most famous patterns present in nature is the Golden Section (GS). It defines an especial proportion that allows the adequate formation, selection, partition, and replication in several natural phenomena. On the other hand, evolutionary algorithms (EAs) are stochastic optimization methods based on the model of natural evolution. One important process in these schemes is the operation of selection which exerts a strong influence on the performance of their search strategy. Different selection methods have been reported in the literature. However, all of them present an unsatisfactory performance as a consequence of the deficient relations between elitism and diversity of their selection procedures. In this paper, a new selection method for evolutionary computation algorithms is introduced. In the proposed approach, the population is segmented into several groups. Each group involves a certain number of individuals and a probability to be selected, which are determined according to the GS proportion. Therefore, the individuals are divided into categories where each group contains individual with similar quality regarding their fitness values. Since the possibility to choose an element inside the group is the same, the probability of selecting an individual depends exclusively on the group from which it belongs. Under these conditions, the proposed approach defines a better balance between elitism and diversity of the selection strategy. Numerical simulations show that the proposed method achieves the best performance over other selection algorithms, in terms of its solution quality and convergence speed. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： evolutionary algorithms Golden Section Selection methods Genetic algorithms (GA) evolutionary strategies (ES) Genetic Programming (GP) evolutionary computation

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Control Optimization for Greenhouse Environment Using evolutionary algorithms

引用

SENSORS 2011年第6期11卷 5792-5807页

作者： Hu, Haigen Xu, Lihong Wei, Ruihua Zhu, Bingkun Zhejiang Agr & Forestry Univ Sch Informat Engn Linan City 311300 Zhejiang Peoples R China Tongji Univ Dept Control Sci & Engn Shanghai 200092 Peoples R China

This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an evolutionary Algorithm (EA) based on multiple performance measures such as good static-dynamic performance specifications and the smooth process of control. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is tested for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a simulation experiment. The results show that by tuning the gain parameters the controllers can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Besides, it can be applied to tuning the system with different properties, such as strong interactions among variables, nonlinearities and conflicting performance criteria. The results implicate that it is a quite effective and promising tuning method using multi-objective optimization algorithms in the complex greenhouse production.

关键词： greenhouse environment control PID control feedback control multi-objective optimization evolutionary algorithms nonlinear systems

来源：评论

学校读者我要写书评

暂无评论

Balancing of flexible rotors based on evolutionary algorithms

引用

MECHANICS & INDUSTRY 2015年第4期16卷 406-1-406-9页

作者： Oke, Wasiu Adeyemi Abido, Mohammad Ali Asafa, Tesleem Babatunde King Fahd Univ Petr & Minerals Dept Mech Engn Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

An unbalance in a rotating flexible rotor causes excessive vibration and elastic deformations with subsequent malfunction and failure. In spite of different techniques deployed to reduce or eliminate rotor unbalance, it is impossible to remove the unbalance completely. The unbalance will only be reduced to a residual level. Hence, any other method that can reduce this residual level further can be considered as an alternative. In this article, Differential Evolution (DE) and Genetic Algorithm (GA) were successfully applied as optimization techniques to balance rotating flexible rotors. The unbalancing challenge is formulated as an optimization problem with an objective function of minimizing the rotor unbalance by identifying the optimum correction parameters. Modeling and response analyses were performed in ANSYS while optimizations were conducted in MATLAB. The results of four balancing cases show that the approaches are robust at both balancing speed and beyond. Also, the results obtained show that GA performs slightly better than DE in terms of optimization time and effective reduction of vibration amplitude.

关键词： Optimization rotor unbalance evolutionary algorithms ANSYS MATLAB

来源：评论

学校读者我要写书评

暂无评论

Optimization of Steel Jackets to Support Offshore Wind Turbines Using evolutionary algorithms

引用

JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING-TRANSACTIONS OF THE ASME 2024年第2期146卷 022001-022001页

作者： Cruz, Rodrigo Oliveira Duarte, Grasiele Regina de Lima, Beatriz Souza Leite Pires Jacob, Breno Pinheiro Fed Univ Rio Janeiro Postgrad Inst COPPE UFRJCivil Engn Dept Cidade UnivAve Pedro CalmonS-N BR-21941596 Rio De Janeiro RJ Brazil

This paper presents an optimization tool for jacket structures to support Offshore Wind Turbines (OWTs). The tool incorporates several combinations of optimization algorithms and constraint-handling techniques (CHTs): Genetic Algorithm;Differential Evolution (DE);Tournament Selection Method;Multiple Constraint Ranking (MCR);Adaptive Penalty Method, and Helper-and-Equivalent Optimization. The objective function regards the minimization of the jacket weight;the design variables are the diameter and thickness of the tubular members. The constraints are related to natural frequencies and Ultimate Limit State criteria. The candidate solutions are evaluated by full nonlinear time-domain Finite Element coupled analyses. To assess the optimization algorithms and CHTs, a case study is presented for the standardized OWT/jacket structure from the Offshore Code Comparison Collaboration Continuation project. First, a numerical model is built and validated, in terms of masses, natural frequencies, and vibration modes;then, this model is employed to run the optimization tool for all combinations of optimization algorithms and CHTs. The results indicate that, while all methods lead to feasible optimal solutions that comply with the constraints and present considerable weight reductions, the best performer is the combination of the DE algorithm with the MCR constraint-handling technique.

关键词： structural optimization evolutionary algorithms offshore jackets offshore wind turbines design of offshore structures ocean energy technology

来源：评论

学校读者我要写书评

暂无评论

A retrofitting method for torsionally sensitive buildings using evolutionary algorithms

引用

EARTHQUAKES AND STRUCTURES 2017年第3期12卷 309-319页

作者： Efstathakis, Nikos C. Papanikolaou, Vassilis K. Aristotle Univ Thessaloniki Lab Reinforced Concrete & Masonry Struct Sch Civil Engn POB 482 Thessaloniki 54124 Greece

A new method is suggested for the retrofitting of torsionally sensitive buildings. The main objective is to eliminate the torsional component from the first two natural modes of the structure by properly modifying its stiffness distribution via selective strengthening of its vertical elements. Due to the multi-parameter nature of this problem, state-of-art optimization schemes together with an ad-hoc software implementation were used for quantifying the required stiffness increase, determine the required retrofitting scheme and finally design and analyze the required composite sections for structural rehabilitation. The performance of the suggested method and its positive impact on the earthquake response of such structures is demonstrated through benchmark examples and applications on actual torsionally sensitive buildings.

关键词： structural optimization evolutionary algorithms retrofitting method torsional sensitivity R/C jackets

来源：评论

学校读者我要写书评

暂无评论

Analysis of the FTIR spectrum of pyrazine using evolutionary algorithms

引用

JOURNAL OF MOLECULAR SPECTROSCOPY 2009年第1期257卷 74-81页

作者： Schmitt, Michael Biemann, Lars Meerts, W. Leo Kleinermanns, Karl Univ Dusseldorf Inst Phys Chem 1 D-40225 Dusseldorf Germany Radboud Univ Nijmegen Mol & Biophys Grp Inst Mol & Mat NL-6500 GL Nijmegen Netherlands

The FTIR spectrum of pyrazine in the gas phase has been measured and analyzed using automated evolutionary algorithms. For the stronger bands, the rotational constants for ground and vibrationally excited states, the correct band types and in some cases centrifugal distortion constants could be extracted. Several hot hands have been identified and assigned by comparison to a cubic force field calculation at the MP2/6-311G(d,p) level of theory. Vibrationally averaged rotational constants for the excited bands can give a further guidance in the assignment of the vibrational bands. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Pyrazine Infrared spectroscopy evolutionary algorithms Ab initio calculations

来源：评论

学校读者我要写书评

暂无评论

A Revisit of Infinite Population Models for evolutionary algorithms on Continuous Optimization Problems

引用

evolutionary COMPUTATION 2020年第1期28卷 55-85页

作者： Song, Bo Li, Victor O. K. Univ Hong Kong Dept Elect & Elect Engn Pokfulam Hong Kong Peoples R China

Infinite population models are important tools for studying population dynamics of evolutionary algorithms. They describe how the distributions of populations change between consecutive generations. In general, infinite population models are derived from Markov chains by exploiting symmetries between individuals in the population and analyzing the limit as the population size goes to infinity. In this article, we study the theoretical foundations of infinite population models of evolutionary algorithms on continuous optimization problems. First, we show that the convergence proofs in a widely cited study were in fact problematic and incomplete. We further show that the modeling assumption of exchangeability of individuals cannot yield the transition equation. Then, in order to analyze infinite population models, we build an analytical framework based on convergence in distribution of random elements which take values in the metric space of infinite sequences. The framework is concise and mathematically rigorous. It also provides an infrastructure for studying the convergence of the stacking of operators and of iterating the algorithm which previous studies failed to address. Finally, we use the framework to prove the convergence of infinite population models for the mutation operator and the k-ary recombination operator. We show that these operators can provide accurate predictions for real population dynamics as the population size goes to infinity, provided that the initial population is identically and independently distributed.

关键词： evolutionary algorithms infinite population models population dynamics convergence in distribution theoretical analysis

来源：评论

学校读者我要写书评

暂无评论

Terminating evolutionary algorithms at their steady state

引用

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2015年第2期61卷 489-515页

作者： Gil, Debora Roche, David Borras, Agnes Giraldo, Jesus Univ Autonoma Barcelona Dept Comp Sci Comp Vis Ctr E-08193 Barcelona Spain Univ Autonoma Barcelona Lab Syst Pharmacol & Bioinformat Inst Neurociencies E-08193 Barcelona Spain Univ Autonoma Barcelona Unitat Bioestadist E-08193 Barcelona Spain

Assessing the reliability of termination conditions for evolutionary algorithms (EAs) is of prime importance. An erroneous or weak stop criterion can negatively affect both the computational effort and the final result. We introduce a statistical framework for assessing whether a termination condition is able to stop an EA at its steady state, so that its results can not be improved anymore. We use a regression model in order to determine the requirements ensuring that a measure derived from EA evolving population is related to the distance to the optimum in decision variable space. Our framework is analyzed across 24 benchmark test functions and two standard termination criteria based on function fitness value in objective function space and EA population decision variable space distribution for the differential evolution (DE) paradigm. Results validate our framework as a powerful tool for determining the capability of a measure for terminating EA and the results also identify the decision variable space distribution as the best-suited for accurately terminating DE in real-world applications.

关键词： evolutionary algorithms Termination condition Steady state Differential evolution

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：