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evolutionary algorithms for aerofoil design

INTERNATIONAL JOURNAL OF COMPUTATIONAL FLUID DYNAMICS 1998年第1-2期11卷 51-77页

作者： De Falco, I Della Cioppa, A Iazzetta, A Tarantino, E Natl Res Council Italy CNR IRSIP I-80131 Naples Italy

This paper wishes to describe evolutionary Algorithms as an effective means for the solution of the Aerofoil Design Optimisation in Aerodynamics. Firstly the basic ideas underlying evolutionary Algorithms are outlined. Several versions of evolutionary Algorithms are briefly described, focussing on their similarities and on their differences as well. Then their application to both Direct and Inverse Aerofoil Design Problem is described, and results are given. Finally, several possible parallel models for evolutionary Algorithms are discussed, and the results of the application of one of them to the above problem are presented.

关键词： optimisation aerofoil design evolutionary algorithms genetic algorithms evolution strategies breeder genetic algorithms evolutionary programming parallel evolutionary algorithms

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Predictive control of intersegmental tarsal movements in an insect

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2017年第1期43卷 5-15页

作者： Costalago-Meruelo, Alicia Simpson, David M. Veres, Sandor M. Newland, Philip L. Univ Southampton Fac Engn & Environm Southampton Hants England Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England Univ Southampton Biol Sci Southampton Hants England Ludwig Maximilians Univ Munchen Neurol Forsch Haus Munich Germany

In many animals intersegmental reflexes are important for postural and movement control but are still poorly undesrtood. Mathematical methods can be used to model the responses to stimulation, and thus go beyond a simple description of responses to specific inputs. Here we analyse an intersegmental reflex of the foot (tarsus) of the locust hind leg, which raises the tarsus when the tibia is flexed and depresses it when the tibia is extended. A novel method is described to measure and quantify the intersegmental responses of the tarsus to a stimulus to the femoro-tibial chordotonal organ. An Artificial Neural Network, the Time Delay Neural Network, was applied to understand the properties and dynamics of the reflex responses. The aim of this study was twofold: first to develop an accurate method to record and analyse the movement of an appendage and second, to apply methods to model the responses using Artificial Neural Networks. The results show that Artificial Neural Networks provide accurate predictions of tarsal movement when trained with an average reflex response to Gaussian White Noise stimulation compared to linear models. Furthermore, the Artificial Neural Network model can predict the individual responses of each animal and responses to others inputs such as a sinusoid. A detailed understanding of such a reflex response could be included in the design of orthoses or functional electrical stimulation treatments to improve walking in patients with neurological disorders as well as the bio/inspired design of robots.

关键词： Reflex Artificial Neural Network Metaheuristic algorithm evolutionary programming Particle swarm optimisation Locust Motor control

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Multiobjective evolutionary algorithm for long-term planning of the national energy and transportation systems

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2011年第2期2卷 151-169页

作者： Ibanez, Eduardo McCalley, James D. Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

The transportation and electric sectors are by far the largest producers of greenhouse emissions in the United States while they consume a significant amount of the national energy. The ever rising demand for these systems, the growing public concern on issues like global warming or national security, along with emerging technologies that promise great synergies between both (plug-in hybrid vehicles or electrified rail), creates the necessity for a new framework for long-term planning. This paper presents a comprehensive methodology to investigate long-term investment portfolios of these two infrastructures and their interdependencies. Its multiobjective nature, based on the NSGA-II evolutionary algorithm, assures the discovery of the Pareto front of solutions in terms of cost, sustainability and resiliency. The optimization is driven by a cost-minimization network flow program which is modified in order to explore the solution space. The modular design enables the use of metrics to evaluate sustainability and resiliency and better characterize the objectives that the systems must meet. An index is presented to robustly meet long-term emission reduction goals. An example of a high level representation of the continental United States through 2050 is presented and analyzed using the present methodology.

关键词： Energy Transportation Long-term investment Multiobjective optimization evolutionary programming

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evolutionary computing approach to optimize superframe scheduling on industrial wireless sensor networks

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第3期34卷 706-715页

作者： Satrya, Gandeva Bayu Shin, Soo Young Telkom Univ Sch Appl Sci Kabupaten Bandung Indonesia Kumoh Natl Inst Technol Dept It Convergence Engn Seoul South Korea

The industrial wireless sensor domain has undergone a shift in paradigm as a consequence of Internet of Things (IoT), a thriving technology that has been leading the way in short-range and fixed wireless sensing. One of the problems associated with industrial wireless sensor networks (IWSNs) is finding the optimal solution for minimizing defect time in superframe scheduling. This paper proposed a method based on the use of evolutionary algorithms, namely particle swarm optimization (PSO), orthogonal learning PSO, genetic algorithm (GA), and modified GA for optimizing the superframe scheduling. Additionally, we evaluated a contemporary method, deadline monotonic scheduling, on the ISA 100.11a protocol. The use of this standard as a case study means that the presented 72 simulations are object-oriented, with numerous variations in the number of timeslots and wireless sensor nodes. The simulation results show that the use of GA and modified GA can improve the performance in terms of idle, missed deadlines, memory consumption, and processing time comparing to other metaheuristic algorithms. A comprehensive analysis and detailed performance evaluation are provided in the paper. (C) 2020 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

关键词： evolutionary programming Genetic algorithm Industrial wireless sensor network (IWSN) Internet of Things (IoT) ISA 100.11a Superframe scheduling

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Integrating Evolving Fuzzy Neural Networks and Tabu Search for short term load forecasting

Integrating Evolving Fuzzy Neural Networks and Tabu Search f...

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IEEE PES Transmission and Distribution Conference and Exposition

作者： Liao, GC Tsao, TP Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung Taiwan

ISBN: (纸本)0780381106

An IntegratedEvolving Fuzzy Neural Network and Tabu Search I(EFNNTS) for short term load forcasting method is presented in this *** this paper, a shortterm load forecasting is presented firstusing Fuzzy Hyper-Rectangular Composite Neural Networks (FHRCNNs). Then, we use evolutionary programming (EP) and Tabu Search (TS) to find the optimal solution of the parameters of FHRRCNNs (that parameters include such as synaptic weights(w(jk)), biases (theta(jk)), membership function (m(j)((x) under bar (t))), sensitivity factor in membership function ((Sj)) and adjustable synaptic weight (M-ij and m(ij)). We know that the EP has a good capability at search globe optimal value, but has poor capability search local optimal. But the TS just has good capability at local optimal search. So, here, we combine this two methods advantages to improve the shortcoming of the tradition training that the weights and biases always trapped into-a 1 cal optimal. Finally, we use this (IEFNNTS) can improve the solution quality. Actually, we can reduce the error of load forecasting. The proposed IEFNNTS load forecasting scheme was test using data obtained from a sample study include one year, month and 24 hours. The result demonstrated the accuracy of the proposed load forecasting scheme.

关键词： load forecasting evolutionary programming tabu search fuzzy neural network

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An Algorithm for Determining Neural Network Architecture Using Differential Evolution

An Algorithm for Determining Neural Network Architecture Usi...

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2nd International Conference on Business Intelligence and Financial Engineering

作者： Bhuiyan, Md Zakirul Alm Cent S Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China

ISBN: (纸本)9780769537054

Artificial Neural Networks (ANNs) have been applied to a variety of classification and learning tasks. The use of evolutionary Algorithms (EA) as one of the fastest, robust and efficient global search techniques has allowed different properties of artificial neural networks to be evolved. This paper proposes the possibility of using differential evolution for Determining an ANN Architecture (DNNA). We explain how to use differential evolution's application for determining an ANN architecture. The approach we describe is innovative and has only been successfully applied and implemented for the first time, although the idea of Differential Evolution has been applied in various fields since the last decade. In this work, we proposed an algorithm based on Differential Evolution that uses a minimum number of user specified parameters in determining an ANN architecture. By using back-propagation algorithm to train the ANN architecture partially during the evolution process, DNNA is evaluated on five benchmark classification problems, namely, Cancer, Diabetes, Heart Disease, Thyroid, and the Australian Credit Card problem. Through performance analysis and simulation studies, we show that DNNA can produce ANN architecture with good generalization abilities, but with less number of training cycles when compared with an evolutionary programming approach and standard back-propagation.

关键词： differential evolution evolutionary algorithm evolutionary programming artificial neural networks

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An artist's experience in using an evolutionary algorithm to produce an animated artwork

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INTERNATIONAL JOURNAL OF ARTS AND TECHNOLOGY 2011年第2期4卷 155-167页

作者： Trist, Karen Ciesielski, Vic Barile, Perry RMIT Univ GPO Box 2476V Melbourne Vic 3001 Australia

We describe an artist's journey of working with an evolutionary algorithm to create an artwork suitable for exhibition in a gallery. Software based on the evolutionary algorithm produces animations which engage the viewer with a target image slowly emerging from a random collection of greyscale lines. The artwork consists of a grid of movies of eucalyptus tree targets. Each movie resolves with different aesthetic qualities, tempo and energy. The artist exercises creative control by choice of target and values for evolutionary and drawing parameters.

关键词： evolutionary programming evolved art new media art software art animation algorithmic art AI genetic programming genetic art NPR non-photorealistic rendering

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evolutionary computation

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WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS 2009年第1期1卷 52-56页

作者： De Jong, Kenneth George Mason Univ Dept Comp Sci Fairfax VA 22030 USA

This article provides a brief overview of the field of evolutionary Computation. It describes the important historical developments that shaped the field. It summarizes the field as it exists today and discusses some ... 详细信息

关键词： evolutionary algorithms genetic algorithms evolutionary programming evolution strategies genetic programming

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EP-based Optimization of Strategic Safety Stocks in Reverse Logistics Systems

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Journal of Donghua University(English Edition) 2004年第5期21卷 65-68页

作者：李军王继荣胡宗武张继会 Shanghai Jiaotong University Shanghai 200030 Qingdao University Qingdao 266071 Qingdao University Qingdao 266071 Shanghai Jiaotong University Shanghai 200030

A new heuristic strategic safety stock optimization is proposed based on evolutionary programming(EP) algorithm for reverse logistics supply chain systems. The supply chain is described with a network and the modeling complexity of external as well as internal product returns and reuses of supply chains is considered with. It is assumed that customer demands for final products are uncertain. Products are randomly returned from external customers to stock points. The optimization model is established and three different cases with different structures are used to show the strength of the algorithm.

关键词： safety stocks supply chain management reverse logistics evolutionary programming

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Model structure selection for a discrete-time non-linear system using a genetic algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2004年第I2期218卷 85-98页

作者： Ahmad, R Jamaluddin, H Hussain, MA Univ Teknol Malaysia Fac Mech Engn Skudai 81310 Johor Malaysia Univ Malaya Fac Engn Dept Chem Engn Kuala Lumpur Malaysia

In recent years, extensive works on genetic algorithms have been reported covering various applications. Genetic algorithms (GAS) have received significant interest from researchers and have been applied to various optimization problems. They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. First the effect of different combinations of GA operators on the performance of the model developed is studied. A proposed algorithm called modified GA, or MGA, is presented and a comparison between a simple GA and a modified GA is carried out. The performance of the proposed algorithm is also compared to the model developed using the orthogonal least squares (OLS) algorithm. The adequacy of the developed models is tested using one-step-ahead prediction and correlation-based model validation tests. The results show that the proposed algorithm can be employed as an algorithm to select the structure of the proposed model.

关键词： model structure selection system identification evolutionary programming genetic algorithms

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