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DESIGNING ROAD NETWORKS FOR TRANSPORT PLANNING USING evolutionary computation

APPLIED ARTIFICIAL INTELLIGENCE 2008年第9期22卷 921-936页

作者： Pinninghoff, Maria Contreras, Ricardo Atkinson, John Univ Concepcion Dept Comp Sci Concepcion Chile

In this article, a new approach to designing optimum urban road networks using evolutionary methods is described. The model is capable of finding a road network that addresses the Private transport, assignment in a certain city area. Experiments using our model in real transport assignment tasks show the promise of the approach to improve existing roads and create new roads as Projected in a rear future.

关键词： ROADS -- Design & construction TRANSPORTATION evolutionary computation NEURAL networks (Computer science) ARTIFICIAL intelligence SIMULATION methods & models AUTOMATION CYBERNETICS URBAN transportation

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Human gait simulation with a neuromusculoskeletal model and evolutionary computation

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JOURNAL OF VISUALIZATION AND COMPUTER ANIMATION 2003年第2期14卷 73-92页

作者： Hase, K Miyashita, K Ok, S Arakawa, Y Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki 3058566 Japan

This paper describes a human gait animation system with a precise neuromusculoskeletal model and evolutionary computation. The neuromusculoskeletal model incorporates 14 rigid bodies, 19 degrees of freedom, 60 muscular models, 16 pairs of the neural oscillators, and other neuronal systems. By changing the search parameters and the evaluative criteria of the evolutionary search process, we demonstrate various locomotive patterns, such as normal! gait, pathological gait, running and ape-like walking. The proposed simulation system takes, not only kinematic data but also in vivo dynamic data such as energy consumption information into consideration, so that the resultant locomotion patterns are natural and valid from a biomechanical point of view. Hence the simulation system can also be used for finding a biologically appropriate physical model to realize a desired gait by simultaneously modifying the body dynamics parameters with the neuronal parameters. This capability creates a novel application of human gait simulation systems, such as rehabilitation tool design and consultation for physically handicapped people. Copyright (C) 2003 John Wiley Sons, Ltd.

关键词： physics-based animation human gait evolutionary computation genetic algorithms neuromusculoskeletal model neural oscillator

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Parallel evolutionary computation in bioinformatics applications

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013年第2期110卷 183-191页

作者： Pinho, Jorge Sobral, Joao Luis Rocha, Miguel Univ Minho Dep Informat Comp Sci & Technol Ctr CCTC P-4710057 Braga Portugal

A large number of optimization problems within the field of Bioinformatics require methods able to handle its inherent complexity (e.g. NP-hard problems) and also demand increased computational efforts. In this context, the use of parallel architectures is a necessity. In this work, we propose ParJECoLi, a Java based library that offers a large set of metaheuristic methods (such as evolutionary Algorithms) and also addresses the issue of its efficient execution on a wide range of parallel architectures. The proposed approach focuses on the easiness of use, making the adaptation to distinct parallel environments (multicore, cluster, grid) transparent to the user. Indeed, this work shows how the development of the optimization library can proceed independently of its adaptation for several architectures, making use of Aspect-Oriented Programming. The pluggable nature of parallelism related modules allows the user to easily configure its environment, adding parallelism modules to the base source code when needed. The performance of the platform is validated with two case studies within biological model optimization. (c) 2012 Elsevier Ireland Ltd. All rights reserved.

关键词： evolutionary computation Parallel software development Aspect oriented programming Bioinformatics

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Ecological relationships, thresholds and time-lags determining phytoplankton community dynamics of Lake Kinneret, Israel elucidated by evolutionary computation and wavelets

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ECOLOGICAL MODELLING 2013年 255卷 70-86页

作者： Recknagel, Friedrich Ostrovsky, Ilia Cao, Hongqing Zohary, Tamar Zhang, Xiaoqing Univ Adelaide Sch Earth & Environm Sci Adelaide SA 5005 Australia Israel Oceanog & Limnol Res Kinneret Limnol Lab IL-14950 Migdal Israel Chinese Acad Sci Res Ctr Ecoenvironm Sci Beijing 100085 Peoples R China

The hybrid evolutionary algorithm (HEA) was implemented to model and analyze population dynamics of the different phytoplankton phyla (chlorophyta, bacillariophyta, cyanophyta and dinophyta) in relation to physical, chemical, and biological determinants and their combinations in a large lake. Biweekly measurements over a 12-year period were used as input. The validation of models obtained with HEA showed the best results for bacillariophyta and dinophyta resulting in coefficients of determination (r(2)) between the modeled and measured data of 0.54-0.79 and 0.29-0.76 for these phyla, respectively, suggesting good predictability of their dynamics. The lowest adequacy of HEA models was found for cyanophyta (r(2) of 0.28-0.46). Models that combined physical, chemical and biological inputs scored highest, whilst zooplankton-based models scored lowest in all experiments and indicated that top-down control of algal biomass could have only secondary effect. The input sensitivity analysis was used for testing the best phytoplankton models with threshold values determining high or low algal biomass and inhibitory-excitatory effects of specific parameters. Wavelets were tested to analyze two extreme cases of dinophyta dynamics in years of its exceptionally high and low developments to gain insights into lag times between the exert of key factor and algae response. Lag times extracted from daily interpolated data of highly correlated inputs of dinophyta in 1998 varied between 2 and 4 days. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.

关键词： Lake Kinneret Phytoplankton community Ecological relationships Ecological thresholds Time lags Forecasting Inductive reasoning evolutionary computation Sensitivity analysis Wavelet analysis

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Disturbance encountered landing system design based on sliding mode control with evolutionary computation and cerebellar model articulation controller

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APPLIED MATHEMATICAL MODELLING 2015年第19期39卷 5862-5881页

作者： Juang, Jih-Gau Yu, Shuai-Ting Natl Taiwan Ocean Univ Dept Commun Nav & Control Engn Keelung 20224 Taiwan

Currently, most aircraft have an automatic landing system (ALS) installed. In normal flight conditions, an aircraft automatic landing system can significantly reduce the pilot's workload. Conventional automatic landing systems are designed by the use of gain scheduling or traditional adaptive control techniques;once the flight conditions or wind disturbance intensity is beyond the limits of the system, the pilot must turn off the automatic landing system and manually take over the aircraft landing procedures. The purpose of this study is to integrate the cerebellar model articulation controller (CMAC) and the sliding mode control (SMC) into the aircraft landing system. Genetic algorithm (GA), particle swarm optimization (PSO) and chaotic particle swarm optimization (CPSO) are used to adjust the parameters of the sliding mode control. The proposed intelligent control system can not only effectively improve the landing system to counter wind disturbance, but also help the pilots guide the aircraft to a safe landing in difficult environments. In addition, Lyapunov theory is applied to derive adaptive learning rules for the control system. Furthermore, the TI C6713 rapid property is utilized to develop an embedded control system for a digital signal processing (DSP) controller. The realization of on-line real-time control can thereby be achieved. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Automatic landing Sliding mode control evolutionary computation CMAC

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Biclustering of expression data with evolutionary computation

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2006年第5期18卷 590-602页

作者： Divina, F Aguilar-Ruiz, JS Tilburg Univ Computat Linguist & AI Sect Tilburg Netherlands Pablo Olavide Univ Polytech Seville Spain

Microarray techniques are leading to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In this work, we address the biclustering of gene expression data with evolutionary computation. Our approach is based on evolutionary algorithms, which have been proven to have excellent performance on complex problems, and searches for biclusters following a sequential covering strategy. The goal is to find biclusters of maximum size with mean squared residue lower than a given delta. In addition, we pay special attention to the fact of looking for high-quality biclusters with large variation, i.e., with a relatively high row variance, and with a low level of overlapping among biclusters. The quality of biclusters found by our evolutionary approach is discussed and the results are compared to those reported by Cheng and Church, and Yang et al. In general, our approach, named SEBI, shows an excellent performance at finding patterns in gene expression data.

关键词： biclustering gene expression data evolutionary computation

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State-of-the-Art of Research on Optimization of Shell and Tube Heat Exchangers by Methods of evolutionary computation

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ARCHIVES OF computationAL METHODS IN ENGINEERING 2021年第4期28卷 2761-2783页

作者： Saldanha, Wagner Henrique Ponce Arrieta, Felipe Raul Soares, Gustavo Luis PUC MG Dept Mech Engn Belo Horizonte MG Brazil IBMEC Belo Horizonte 300 Rio Grande do Norte St BR-30130130 Belo Horizonte MG Brazil PUC MG Dept Elect Engn Belo Horizonte MG Brazil

This paper presents a comprehensive state-of-the-art review of optimization by evolutionary computation methods of shell and tubes heat exchangers (STHE) of single segmental baffles. It is seen that the heat transfer coefficient to the shell side is calculated by the Kern method or by the Bell Delaware method, and that the pressure drop to the shell side is calculated by the Kern method or by the Bell Delaware method or by the Peters and Timmershaus method. It is verified the use of evolutionary computation algorithms in single-objective and multiobjective optimization of STHE, and that most related work used the total cost function of STHE. And among the contributions presented, there were implementations and applications of different evolutionary computation algorithms, or study of the STHE optimization problem by different objective functions, such as ecological, entransy dissipation, field synergy, and cost of the life cycle. Also, it was possible to verify some gaps in the work related to STHE optimization, among them, can be cited the need for greater application of decision-making methods that relate technical, economic and managerial aspects applied to the solution of the STHE. And it is suggested for future work the development of the STHE model subject to delimited uncertainties;and the formalization of a standard STHE optimization problem, so that it can serve as a reference for comparing the different optimization algorithms, and applying metrics to evaluate the advantages that are obtained when using a new algorithm for the same problem.

关键词： evolutionary computation Optimization Shell and tubes heat exchangers

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Social Web in IoT: Can evolutionary computation and Clustering Improve Ontology Matching for Social Web of Things?

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IEEE TRANSACTIONS ON computationAL SOCIAL SYSTEMS 2024年第3期11卷 3966-3977页

作者： Belhadi, Asma Djenouri, Djamel Djenouri, Youcef Belbachir, Ahmed Nabil Srivastava, Gautam OsloMet Univ N-0130 Oslo Norway Univ West England CSRS Comp Sci & Creat Technol Dept Bristol BS16 1QY Avon England Univ South Eastern Norway N-3616 Kongsberg Norway Norwegian Res Ctr Oslo Norway IDEAS NCBR Warsaw Poland Norwegian Res Ctr N-4879 Grimstad Norway Brandon Univ Dept Math & Comp Sci Brandon MB R7A 6A9 Canada China Med Univ Res Ctr Interneural Comp Taichung 40402 Taiwan Lebanese Amer Univ Dept Comp Sci & Math Beirut 1102 Lebanon

Many Internet of Things (IoT) applications can benefit from Social Web of Things (S-WoT) methods that enable knowledge discovery and help solving interoperability problems. The semantic modeling of S-WoT is the main emphasis of this work where we suggest a novel solution, evolutionary clustering for ontology matching (ECOM), to explore correlations between S-WoT data using clustering and evolutionary computation methodologies. The ECOM approach uses a variety of clustering techniques to aggregate S-WoT data's strongly related ontologies into comparable categories. The principle is to match concepts of similar groups rather than full concepts of two ontologies, which necessitates splitting examples of each ontology into similar groups. We design two clustering algorithms for ontology matching using conventional methods, as well as sophisticated clustering techniques. Moreover, we develop an intelligent matching algorithm that uses evolutionary computation to quickly converge to (or ideally identify) optimal matches. Numerous simulations have been conducted using various ontology databases to demonstrate the application and precision of ECOM. Our findings clearly show that ECOM has better results when compared to cutting-edge ontology matching methods. The F-measure of ECOM exceeds 95% whereas it does not reach 90% for all baseline methods. The results also confirm that ECOM scales with big data in S-WoT environments.

关键词： Clustering evolutionary computation ontology matching Social Web of Things

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Optimal tuning of H∞ fixed-structure robust controller against multiple high-level requirements using evolutionary computation

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2019年第4期29卷 949-972页

作者： Feyel, P. Duc, G. Sandou, G. Safran Elect & Def Massy France Univ Paris Saclay Univ Paris Sud Lab Signaux & Syst L2S Cent SupelecCNRSUMR 8506 Gif Sur Yvette France

This work deals with the optimal controller synthesis against high-level multiple requirements using evolutionary computation. Indeed, such stochastic algorithms are interesting to solve problems based on complex industrial specifications and so seem to be particularly well suited to optimal robust controller synthesis. Using the H-infinity loop-shaping framework, the optimal weights/controller tuning without any structural assumption (in terms of poles/zeros/damping) on the searched filters (except of course their order) is investigated. The absence of such any structural assumption is important to avoid affecting the quality of the solution toward a complex specification and allows reducing the synthesis problem to a simple one with static scalings in place of frequency weights. Using a version of differential evolution algorithm well adapted for high dimensional control problems, computing directly a fixed-structure controller for complex industrial specifications toward a generic nonconstraint fitness with quite reasonable computing time is achieved. The illustrating example deals with the line-of-sight stabilization problem.

关键词： constraints handling evolutionary computation H-infinity optimization robust control

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High-Dimensional Feature Selection for Power System Congestion Event Prognosis With Enhanced evolutionary computation

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第1期39卷 1752-1770页

作者： Xu, Jian Jiang, Xinxiong Liao, Siyang Ke, Deping Sun, Yuanzhang Yao, Liangzhong Wuhan Univ Sch Elect Engn & Automat Hubei Engn & Technol Res Ctr AC DC Intelligent Di Wuhan 430072 Peoples R China

Power system congestion event prognosis (CEP) based on multivariate time series (MTS) learning is an effective way to improve the warning abilities against risky situations. However, it is still challenging to decide which measurement sequences of system variables should be selected to train MTS learning-based CEPs models, especially when facing high-dimensional candidate features from power systems. Focusing on this issue, this paper proposes a novel high-dimensional feature selection (FS) method to identify and select variables that contain beneficial data patterns for the prognosis of network congestion events. A wrapper framework embedded with multiple MTS learning models is first built to treat FS as a combinatorial optimization task and receive MTS as inputs directly without sequence compressions. Then, to improve the combinatorial optimization efficiency and FS results in the high-dimensional case, a hybrid structure called enhanced evolutionary computation (EEC) is developed by combining with information theory-based feature priority scores, which play the role of semi-guidance in the iterative search. Test results on both the real-world and synthetic datasets validate the effectiveness of the developed EEC structure and show that the selected features by the proposed FS method are more beneficial in identifying the early patterns of network congestion events, thus contributing to efficient and accurate CEP for power systems.

关键词： computational modeling Feature extraction Prognostics and health management Time series analysis Power systems Optimization Data models Congestion event prognosis early warning evolutionary computation feature selection multivariate time series

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