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Building Information Modeling Steered by evolutionary computing

JOURNAL OF computing IN CIVIL ENGINEERING 2014年第4期28卷 05014003-05014003页

作者： Rafiq, M. Y. Rustell, M. J. Univ Plymouth Sch Marine Sci & Engn Reynolds Bldg Plymouth PL4 8AA Devon England Univ Surrey Ctr Environm Strategy Guildford GU2 7XH Surrey England

In recent years, there has been a surge in the use of building information modeling (BIM) within construction. Much of the previous published research focuses on the impact of BIM during the detailed design, construction, and postconstruction stages. This paper combines the search and exploration powers of evolutionary computing with BIM during the conceptual stage of design. The main contribution of this paper is a methodology to integrate solutions generated by the interactive and visual clustering genetic algorithm (IVCGA) into a BIM environment to enhance the design information and allow the solutions to be viewed in greater detail as building information models. These models can then be utilized or modified by individual members of the design team to aid in the selection of concept designs that appropriately meet client/design requirements. An Autodesk Revit plug-in application has been developed that enables the IVCGA solution to be transformed into a building information model in a common repository. Using this approach during the conceptual design stage can potentially enhance multidisciplinary decision-making capacity by allowing a wide range of concept designs to be assessed in high detail with more accurate costing prior to committing a large sum of funding to the project (C) 2014 American Society of Civil Engineers.

关键词： Building information modeling evolutionary computing Genetic algorithm Interactive visualization Multidisciplinary optimization

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Towards Improving Numerical Weather Predictions by evolutionary computing Techniques

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Procedia Computer Science 2012年 9卷 1056-1063页

作者： Hisham Ihshaish Ana Cort́es Miquel A. Senar Departament d’Arquitectura de Computadors i Sistemes Operatius Escola d’Enginyeria Universitat Aut‘onoma de Barcelona 08193 Bellaterra (Barcelona) Spain

Weather forecasting is complex and not always accurate, moreover, it is generally deﬁned by its very nature as a process that has to deal with uncertainties. In a previous work, a new weather prediction scheme was presented, which uses evolutionary computing methods, particularly, Genetic Algorithms in order to ﬁnd the most timely ‘optimal’ values of model closure parameters that appear in physical parametrization schemes which are coupled with numerical weather prediction (NWP) models. Currently, these parameters are speciﬁed manually. Our hypothesis is that the NWP model forecast skill is sensitive to the speciﬁed parameter values. And thus, by ﬁnding ‘optimal’ values of these parameters, we aim to enhance prediction quality. In this work however, the same scheme is extended by introducing different ways of prediction evaluation during the process of searching closure parameter values. To verify our new scheme, we show prediction results of an experimental case using historical data of a well known weather catastrophe: Hurricane Katrina that occurred in 2005 in the Gulf of Mexico. Obtained results provide signiﬁcant enhancement in weather prediction.

关键词： numerical weather prediction evolutionary computing ensemble prediction parameter estimation

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Mobile wireless sensor network lifetime maximization by using evolutionary computing methods

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AD HOC NETWORKS 2020年 101卷 102094-000页

作者： Zhang, Xiu Lu, Xiaohui Zhang, Xin Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin 300387 Peoples R China

Due to the continuous development and progress of wireless communication technology and sensor network technology, wireless sensor networks (WSNs) have gradually become an attractive technology that facilitates people's lives. Due to the extensive use of WSNs, maximizing the lifetime of WSNs to obtain real-time and effective information has become a critical concern. This paper studies the life of mobile wireless sensor networks (MWSNs). MWSNs are a special type of WSN in that the sensor nodes are movable within a certain area. A system model is developed to prolong the network lifetime of MWSNs. This paper uses five evolutionary computing (EC) algorithms to develop the MWSN lifetime optimization model. Numerical simulations are performed to study the advantages and disadvantages of the five algorithms for solving the model. The comparison and discussion can provide advice for using EC algorithms to solve MWSN lifetime maximization problems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Wireless sensor network Lifetime maximization evolutionary computing Energy efficient Optimization

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Identifying Self-excited Vibrations with evolutionary computing

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Procedia Computer Science 2014年 29卷 637-647页

作者： Christiaan D. Erdbrink Valeria V. Krzhizhano vskaya University of Amsterdam Amsterdam The Netherlands ITMO University Saint Petersburg Russia Deltares Delft The Netherlands Saint Petersburg State Polytechnic University Russia

This study uses differential evolution to identify the coeffic ients of second-order differentia l equations of self-e xc ited vibrations fro m a time signal. The motivation is found in the ample occurrence of this vibration type in engineering and physics, in particu lar in the real -life proble m of v ibrations of hydraulic structure gates. In the proposed method, an equation structure is assumed at the level of the ordinary differentia l equation and a population of candidate coefficient vectors undergoes evolutionary training. In this way the numerical constants of non-linear terms of various self-e xc ited vibration types were recovered fro m the time signal and the velocity value only at the initial t ime. Co mparisons are given regarding accuracy and computing time. Dependency of the test errors on the algorith m para meters is studied in a sensitivity analysis. The presented evolutionary method shows good promise for future applicat ion in engineering systems, in particular operational early -wa rning systems that recognise oscillations with negative damping before they can cause damage.

关键词： evolutionary computing system identification self-excited vibrations differential evolution

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Pitch tracking algorithm based on evolutionary computing with regularisation in very low SNR

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JOURNAL OF ENGINEERING-JOE 2018年第16期2018卷 1509-1514页

作者： Zhang, Xiaoheng Li, Yongming Chongqing Radio & TV Univ Chongqing 400052 Peoples R China Chongqing Univ Commun Engn Chongqing 400030 Peoples R China

The authors present PTEAR_VLSNR (Pitch Tracking basing on evolutionary Algorithm with Regularization at Very Low SNR), a pitch tracking algorithm for speech in strong noise. The algorithm builds a pitch enhancement and extraction model, which enhance the pitch by a matched filter, and to further deal with strong noise, the optimal factor was proposed, which can be optimised globally by the evolutionary computing. Specially, regularisation constraint of fitness function was applied to enhance the generalisation ability. Temporal dynamics constraints are used to improve the tracking rate and the voicing decision can be optimal by evolutionary computing similarly. In addition, the balance of optimisation accuracy and time cost were considered. In experiments, genetic algorithm and particle swarm optimisation with two-norm term were represented as evolutionary algorithms with regularisation. At last, they compare the performance of the algorithm and other representative algorithms. The experimental results show that this proposed algorithm performs well in both high and low signal-to-noise ratios (SNRs).

关键词： speech processing particle swarm optimisation genetic algorithms target tracking pitch tracking algorithm evolutionary computing pitch enhancement extraction model regularisation constraint tracking rate genetic algorithm evolutionary algorithms representative algorithms very low SNR PTEAR_VLSNR PTEAR_VLSNR matched filter voicing decision particle swarm optimisation two-norm term high signal-to-noise ratios

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Pool Evolution: A Parallel Pattern for evolutionary and Symbolic computing

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INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 2016年第3期44卷 531-551页

作者： Aldinucci, Marco Campa, Sonia Danelutto, Marco Kilpatrick, Peter Torquati, Massimo Univ Turin Dept Comp Sci Turin Italy Univ Pisa Dept Comp Sci Pisa Italy Queens Univ Belfast Dept Comp Sci Belfast BT7 1NN Antrim North Ireland

We introduce a new parallel pattern derived from a specific application domain and show how it turns out to have application beyond its domain of origin. The pool evolution pattern models the parallel evolution of a population subject to mutations and evolving in such a way that a given fitness function is optimized. The pattern has been demonstrated to be suitable for capturing and modeling the parallel patterns underpinning various evolutionary algorithms, as well as other parallel patterns typical of symbolic computation. In this paper we introduce the pattern, we discuss its implementation on modern multi/many core architectures and finally present experimental results obtained with FastFlow and Erlang implementations to assess its feasibility and scalability.

关键词： Parallel design patterns Algorithmic skeletons Multi/many core architectures evolutionary computing FastFlow

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An evolutionary computing Model Based on Parallel Architecture

An Evolutionary Computing Model Based on Parallel Architectu...

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2010 International Conference on Computer and Communication Technologies in Agriculture Engineering (CCTAE 2010)

作者： Xiaogang Wang Wuhan University of Science and Engineering Wuhan City, Hubei Province, China 430073 Yan Bai Wuhan University of Science and Engineering Wuhan City, Hubei Province, China 430073 Yue Li Wuhan University of Science and Engineering Wuhan City, Hubei Province, China 430073

we propose a Parallel evolutionary computing model, called CLA-EC, in this paper. In this new model, each genome is assigned to a cell of cellular learning automata to each of which a set of learning automata is assigned. The set of actions selected by the set of automata associated to a cell determines the genome’s string for that cell. Based on a local rule, a reinforcement signal vector is generated and given to the set learning automata residing in the cell. Based on the received signal, each learning automaton updates its internal structure according to a learning algorithm. The process of action selection and updating the internal structure is repeated until a predetermined criterion is met. This model can be used to solve optimization problems. To show the effectiveness of the proposed model it has been used to solve several optimization problems such as real valued function optimization and clustering problems. Computer simulations have shown the effectiveness of this model.

关键词： evolutionary computing Genetic Algorithm Parallel Architecture

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Cache replacement solutions by evolutionary computing technique 09

Cache replacement solutions by evolutionary computing techni...

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Proceedings of the 2009 Spring Simulation Multiconference

作者： Kayvan Tirdad Fatemeh Pakzad Abdolreza Abhari Ryerson University Toronto CA

As we know Internet have grown rapidly in the past few years, and one of the prevalent problems of World Wide Web is increasing network traffic. It has been shown that caching is valuable technique that reduces network traffic. However, still it has some problems such as performance problem. This paper proposes a novel way to solve those problems efficiently by introducing the evolutionary computing technique for cache replacement policy.

关键词： genetic algorithm genetic programming cache replacement evolutionary computing

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An evolutionary computing Model Based on Parallel Architecture

An Evolutionary Computing Model Based on Parallel Architectu...

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2010 International Conference on Computer and Communication Technologies in Agriculture Engineering(计算机与通信技术在农业工程国际会议 CCTAE 2010)

作者： Xiaogang Wang Yan Bai Yue Li Wuhan University of Science and Engineering Wuhan City Hubei Province China 430073

we propose a Parallel evolutionary computing model, called CLA-EC, in this paper. In this new model, each genome is assigned to a cell of cellular learning automata to each of which a set of learning automata is assigned. The set of actions selected by the set of automata associated to a cell determines the genome's string for that cell. Based on a local rule, a reinforcement signal vector is generated and given to the set learning automata residing in the cell. Based on the received signal, each learning automaton updates its internal structure according to a learning algorithm. The process of action selection and updating the internal structure is repeated until a predetermined criterion is met. This model can be used to solve optimization problems. To show the effectiveness of the proposed model it has been used to solve several optimization problems such as real valued function optimization and clustering problems. Computer simulations have shown the effectiveness of this model.

关键词： evolutionary computing Genetic Algorithm Parallel Architecture

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Novel approach to content based image retrieval using evolutionary computing

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Research Journal of Applied Sciences, Engineering and Technology 2014年第6期8卷 691-701页

作者： Imran, Muhammad Hashim, Rathiah Khalid, Noor Elaiza Abd University Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johor 86400 Malaysia Universiti Teknologi MARA Malaysia

Content Based Image Retrieval (CBIR) is an active research area in multimedia domain in this era of information technology. One of the challenges of CBIR is to bridge the gap between low level features and high level semantic. In this study we investigate the Particle Swarm Optimization (PSO), a stochastic algorithm and Genetic Algorithm (GA) for CBIR to overcome this drawback. We proposed a new CBIR system based on the PSO and GA coupled with Support Vector Machine (SVM). GA and PSO both are evolutionary algorithms and in this study are used to increase the number of relevant images. SVM is used to perform final classification. To check the performance of the proposed technique, rich experiments are performed using coral dataset. The proposed technique achieves higher accuracy compared to the previously introduced techniques (FEI, FIRM, simplicity, simple HIST and WH). © Maxwell Scientific Organization, 2014.

关键词： CBIR evolutionary computing Genetic algorithm PSO SVM

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