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ASME Turbine Technical Conference and Exposition (Turbo Expo)

作者： Tang, Xiao Luo, Jiaqi Liu, Feng Peking Univ Dept Aeronaut & Astronaut Beijing 100871 Peoples R China Univ Calif Irvine Dept Mech & Aerosp Engn Irvine CA 92697 USA

ISBN: (纸本)9780791850794

We present an optimization platform for turbomachineries with complex mesh configuration in a parallel computation environment. A continuous adjoint solver for 3-D viscous internal flow is coded under the same parallel framework as the flow solver. To meet the various permitted extents of reshaping on blade surface and cut down the computation cost in grid perturbation, a localized two-level mesh deformation method is developed based on Gaussian radial basis function (RBF). This method works efficiently for both the 0 mesh surrounding the blade and the O-H mesh blocks inside tip gap. In optimization of the transonic NASA Rotor 67 for higher adiabatic efficiency with a mass flow rate constraint, an adjoint sensitivity analysis is conducted. The relations between the design sensitive regions and physical phenomena in internal flow are discussed. Flow fields before and after adjoint optimization are investigated, including shock system, tip leakage flow and flow separation.

关键词： Machinery Turbulence Simulation Cascades (Fluid dynamics) Model development Wakes High pressure (Physics) Stress Turbine blades evolutionary algorithms Anisotropy Eddies (Fluid dynamics) Viscosity Probes Tensors Turbulent diffusion algorithms Stress-strain relations Approximation

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Enhancing the Performance of a Multiobjective evolutionary Algorithm for Sanitary Sewer Overflow Reduction

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2017年第7期143卷 1-9页

作者： Ogidan, Olufunso S. Giacomoni, Marcio Univ Texas San Antonio Environm Sci & Engn Program Dept Civil & Environm Engn One UTSA Blvd San Antonio TX 78249 USA Univ Texas San Antonio Dept Civil & Environm Engn One UTSA Blvd San Antonio TX 78249 USA

The application of multiobjective evolutionary algorithms (MOEAs) to sanitary sewer overflow (SSO) optimization problems typically requires multiple runs of a simulation model and can be very computationally expensive. There is a need for simulation-optimization models that use fewer functional evaluations of the hydraulic model to identify near optimal solutions. In this study, two conflicting objectives were analyzed: maximizing SSO reduction and minimizing rehabilitation cost. This paper introduces a novel MOEA, the enhanced nondominated sorting evolution strategy (eNSES) that uses a specialized operator to guide the algorithm toward known SSOs locations. This strategy is being tested in an existing network in the eastern San Antonio Water System network. It has been compared with NSGA-II and NSES based on hypervolume and the overall nondominated vector generation ratio (ONVGR). The results show that eNSES improves the convergence rate by approximately 70% over the tested alternative algorithms, performing as well as NSGA-II and outperforming NSES in terms of the hypervolume by nearly 10%. In terms of the ONVGR, eNSES performed similarly to NSES but outperformed NSGA-II by 42%. (C) 2017 American Society of Civil Engineers.

关键词： evolutionary algorithms SANITARY sewer overflow HYDRAULIC models REHABILITATION

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Applying Copeland Voting to Design an Agent-Based Hyper-Heuristic 16

Applying Copeland Voting to Design an Agent-Based Hyper-Heur...

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16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

作者： de Carvalho, Vinicius Renan Sichman, Jaime Simao Univ Sao Paulo Comp Engn Dept Intelligent Tech Lab Sao Paulo Brazil

ISBN: (纸本)9781510855076

Meta-heuristics are algorithms which are applied to solve problems when conventional algorithms can not find good solutions in reasonable time;evolutionary algorithms are perhaps the most well-known examples of meta-heuristics. As there are many possible meta-heuristics, finding the most suitable meta-heuristic for a given problem is not a trivial task. In order to make this choice, one can design hyperheuristics. In the literature, one can find some agent-based research whose focus is to propose a framework where metaheuristics are considered as agents, that solve a given problem in a collaborative or competitive way. Most of these works focus on mono-objective meta-heuristics. Other works focus on how to select multi-objective meta-heuristics, but not using an agent-based approach. We present in this work an agent-based hyper-heuristic for choosing the most suitable evolutionary meta-heuristic for a given problem. Our approach performs a cooperative Copeland voting procedure, considering five different metrics, to de fine which one of three competitive evolutionary meta-heuristics should execute during a certain processing time. We use the Walking Fish Problem (WFG) suite with two and three objectives to analyse the proposed approach performance. The obtained results showed that in all cases our strategy found the most indicated evolutionary algorithm and gets competitive results against the state of art.

关键词： Meta-heuristics Hyper-heuristics evolutionary algorithms Self-organization Complex systems Analysis of agent-based simulations Agent cooperation

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A Computer Based Model in Comparison with Sonography Imaging to Diagnosis of Acute Appendicitis in Iran

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JOURNAL OF ACUTE MEDICINE 2017年第1期7卷 10-18页

作者： Jamshidnezhad, Amir Azizi, Ahmad Zadeh, Sara Rekabeslami Shirali, Saeed Shoushtari, Maryam Hadadzadeh Sabaghan, Yalda Ziagham, Vahideh Attarzadeh, Mahsa Ahvaz Jundishapur Univ Med Sci Fac Allied Hlth Sci Dept Hlth Informat Technol Golestan Ave Ahvaz Khuzestan Iran Ahvaz Jundishapur Univ Med Sci Fac Allied Hlth Sci Dept Lab Sci Ahvaz Iran Ahvaz Jundishapur Univ Med Sci Fac Med Ahvaz Iran Ahvaz Imam Educ Hosp Ahvaz Iran

Introduction: Acute appendicitis overlaps with conditions of other diseases in terms of Symptoms and signs in the first hours of presentation. Ultrasound imaging and laboratory tests are usually used to decrease the diagnosis errors in the case of abdominal pain. However, same results may be happened using the mentioned examination tools for a string of diseases with abdominal pain. Moreover, those tests raise the medical costs for hospitals and patients. Clinical Decision Support Systems (CDSSs) can be used to assist the physicians to make the proper health care decisions particularly in the unreliable conditions. Objectives: To improve the decision making process by physicians in diagnosis of acute appendicitis, an optimizing model was developed. The main objective is to discover a diagnostic model using the minimum clinical factors available in the first hours of abdominal pain. Methods: Fuzzy-rule based classifier is a known technique in the Decision Support Systems (DSSs). In this article thus the useful clinical factors were explored and the diagnosis knowledge was discovered using Honey Bee Reproduction Cycle (HRBC) algorithm in the Fuzzy-rule based system. In this model, the proposed algorithm created the Fuzzy rules as the diagnosis knowledge in an optimizing process. To evaluate the accuracy of the proposed model for diagnosing of appendicitis, a collection of data was gathered from abdominal patients who referred to the educational general hospitals in Ahvaz, Iran in 2014 to 2015 years. In this process, the proposed model was optimized first in a training phase using a training dataset, and then it was tested with the testing dataset. Then, the achieved results from the computer base model were compared with ultrasound imaging findings before surgery' as well as other detection methods in the previous studies. Results: The comparison results illustrated that the proposed hybrid classification model as a CDSS improves considerably the accuracy of acute app

关键词： acute appendicitis clinical decision support systems evolutionary algorithms fuzzy systems genetic algorithms

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Optimization of interval type-2 fuzzy logic controllers using evolutionary algorithms

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SOFT COMPUTING 2011年第6期15卷 1145-1160页

作者： Castillo, O. Melin, P. Alanis, A. Montiel, O. Sepulveda, R. Inst Technol Tijuana BC Mexico IPN Ctr Res Digital Syst Tijuana BC Mexico

A method for designing optimal interval type-2 fuzzy logic controllers using evolutionary algorithms is presented in this paper. Interval type-2 fuzzy controllers can outperform conventional type-1 fuzzy controllers when the problem has a high degree of uncertainty. However, designing interval type-2 fuzzy controllers is more difficult because there are more parameters involved. In this paper, interval type-2 fuzzy systems are approximated with the average of two type-1 fuzzy systems, which has been shown to give good results in control if the type-1 fuzzy systems can be obtained appropriately. An evolutionary algorithm is applied to find the optimal interval type-2 fuzzy system as mentioned above. The human evolutionary model is applied for optimizing the interval type-2 fuzzy controller for a particular non-linear plant and results are compared against an optimal type-1 fuzzy controller. A comparative study of simulation results of the type-2 and type-1 fuzzy controllers, under different noise levels, is also presented. Simulation results show that interval type-2 fuzzy controllers obtained with the evolutionary algorithm outperform type-1 fuzzy controllers.

关键词： Interval type-2 fuzzy logic evolutionary algorithms Fuzzy control

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An Artificial Player for a Turn-Based Strategy Game 1

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9th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Maly, Filip Kriz, Pavel Mrazek, Adam Univ Hradec Kralove Fac Informat & Management Dept Informat & Quantitat Methods Hradec Kralove Czech Republic

ISBN: (数字)9783319544724

ISBN: (纸本)9783319544724

This paper describes the design of an artificial intelligent opponent in the Empire Wars turn-based strategy computer game. Several approaches to make the opponent in the game, that has complex rules and a huge state space, are tested. In the first phase, common methods such as heuristics, influence maps, and decision trees are used. While they have many advantages (speed, simplicity and the ability to find a solution in a reasonable time), they provide rather average results. In the second phase, the player is enhanced by an evolutionary algorithm. The algorithm adjusts several parameters of the player that were originally determined empirically. In the third phase, a learning process based on recorded moves from previous games played is used. The results show that incorporating evolutionary algorithms can significantly improve the efficiency of the artificial player without necessarily increasing the processing time.

关键词： Artificial intelligence Computer games Influence maps evolutionary algorithms Heuristics

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A Genetic Algorithm Based Feature Selection for Binary Phenotype Prediction Using Structural Brain Magnetic Resonance Imaging 13

A Genetic Algorithm Based Feature Selection for Binary Pheno...

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Ma, Benteng Xia, Yong Northwestern Polytech Univ Sch Comp Sci Shaanxi Key Lab Speech & Image Informat Proc SAII Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ CMCC Sch Comp Sci Xian 710072 Shaanxi Peoples R China

ISBN: (纸本)9781538621653

Machine learning methods have been increasingly applied to structural brain magnetic resonance imaging (MRI) scans for predicting clinical phenotypes at the individual level. Despite significant methodological developments, reducing the dimensionality of the features extracted from brain MRI data remains a major challenge. In this paper, we propose a genetic algorithm based feature selection approach to binary phenotype prediction using structural brain MRI. We divide the population of individuals into multiple tribes and modify the initialization and evolutionary operations to ensure that the number of selected features in each tribe follows a Gaussian distribution. Thus each tribe is able to focus on exploring a specific part of the solution space. We also incorporate tribe competition into the evolution process, which allows the tribe that produces better individuals to enlarge its sizes so as to have more individuals to search the sub solution space it explores. We have evaluated our proposed approach against eight wrapper and nine filter feature selection methods on the binary phenotype prediction dataset used in the MICCAI 2014 Machine Learning Challenge. Our results indicate that the proposed approach can identify the optimal feature subset more effectively and is able to produce more accurate binary phenotype prediction.

关键词： feature selection pattern classification evolutionary algorithms tribe competition

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Correlation-agnostic fusion for improved uncertainty estimation in multi-view geo-location from UAVs 7

Correlation-agnostic fusion for improved uncertainty estimat...

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Conference on Geospatial Informatics, Fusion, and Motion Video Analytics VII

作者： Taylor, Clark N. Sundlie, Paul O. US Air Force Res Lab Wright Patterson AFB OH 45433 USA Univ Dayton Res Inst Dayton OH 45469 USA

ISBN: (数字)9781510609006

ISBN: (纸本)9781510608993;9781510609006

When geo-locating ground objects from a UAV, multiple views of the same object can lead to improved geolocation accuracy. Of equal importance to the location estimate, however, is the uncertainty estimate associated with that location. Standard methods for estimating uncertainty from multiple views generally assume that each view represents an independent measurement of the geo-location. Unfortunately, this assumption is often violated due to correlation between the location estimates. This correlation may occur due to the measurements coming from the same platform, meaning that the error in attitude or location may be correlated across time;or it may be due to external sources (such as GPS) having the same error in multiple aircraft. In either case, the geo-location estimates are not truly independent, leading to optimistic estimates of the geo-location uncertainty. For distributed data fusion applications, correlation-agnostic fusion methods have been developed that can fuse data together regardless of how much correlation may be present between the two estimates. While the results are generally not as impressive as when correlation is perfectly known and taken into account, the fused uncertainty results are guaranteed to be conservative and an improvement on operating without fusion. In this paper, we apply a selection of these correlation-agnostic fusion techniques to the multi-view geo-location problem and analyze their effects on geo-location and predicted uncertainty accuracy. We find that significant benefits can be found from applying these correlation agnostic fusion effects, but that they vary greatly in how well they estimate their own uncertainty.

关键词： LIDAR Buildings Data modeling Sensors 3D modeling Neural networks Algorithm development evolutionary algorithms Machine learning Fuzzy logic Data fusion Unmanned aerial vehicles

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Integrating Safety-Aware Route Optimisation and Run-Time Safety Monitoring in Controlling Swarms of Drones 28

Integrating Safety-Aware Route Optimisation and Run-Time Saf...

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28th IEEE International Symposium on Software Reliability Engineering

作者： Majd, Amin Trboubitsyna, Elena Abo Akad Univ Fac Sci & Technol Turku Finland

ISBN: (纸本)9781538623879

Swarm of drones are increasingly deployed to perform a variety of critical missions such as surveillance, rescue in disaster areas etc. To guarantee success of a mission, the controlling software should pursue two goals. Firstly, it should ensure safety, i.e., guarantee collision avoidance. Secondly, it should prevent a premature depletion of the batteries of the drones by minimizing their travel paths. In this paper, we propose an approach that combines run-time safety monitoring and high performance evolutionary algorithm to predict dynamically emerging hazards. High performance of the route calculation algorithm allows us to ensure that the routes of drones are dynamically adjusted to avoid collisions while maintaining efficiency. The benchmarking of the proposed approach validates its efficiency and safety.

关键词： safety collision avoidance evolutionary algorithms route planning dynamic safety assurance

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Optimal Design of Engineering Structures 13

Optimal Design of Engineering Structures

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Muc, Aleksander Cracow Univ Technol Inst Machine Design Krakow Poland

ISBN: (纸本)9781538621653

The basic information required to utilize one of possible computation tools/algorithms (mainly the evolution strategy) to solve a wide class of real practical engineering optimization problems is presented and discussed in the present paper. The effectiveness of the considered method is demonstrated by the possibility of the use of different form of objective functions, various and numerous nonlinear constraints and different types of design variables (continuous, discrete, real, integer). The sensitivity of the algorithm to the choice of the evolution strategy parameters is also discussed herein. The generality of the evolution strategy is illustrated by the analysis of three examples dealing with: the design of helical springs, the buckling of cylindrical composite panels and the buckling of pressure vessels with domed heads.

关键词： optimal design evolutionary algorithms buckling cylindrical composite panels pressure vessels helical springs

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