Two-Dimensional Airfoil Shape Optimization Using Highly Differentiable Splines and Evolution Strategies

2016 ◽  
Author(s):  
David Cadrecha ◽  
Jose M. Chaquet ◽  
Roque Corral ◽  
Guillermo Pastor
Keyword(s):  
Mach Number ◽  
Optimization Problems ◽  
Fitness Function ◽  
Evolutionary Process ◽  
Optimization Method ◽  
Design Tool ◽  
Evolution Strategies ◽  
Optimization Approach ◽  
Two Dimensional ◽  
Integral Boundary

An optimization method based on Covariance Matrix Adaptation Evolution Strategies (CMA-ES) is applied on a parametric design tool for the automated generation of two-dimensional turbomachinery airfoil sections. Highly differentiable curves are managed to ensure continuity in the slope of the curvature on the blade surface to avoid undesired anomalies in the Mach number distributions. An Euler solver coupled with an integral boundary layer method is employed to assess the aerodynamic behavior of the geometries. Special care has been made defining several cost functions to allow the algorithm handle unfeasible geometries that can appear during the evolutionary process. The fitness function of feasible individuals can be set up to fulfill several geometric and aerodynamic constraints. To show the potential of the method, several optimization problems have been solved, tracing existing geometries originally defined in a point wise fashion, and applying inverse design to match target Mach number distributions. This method can facilitate the two-dimensional airfoil design and can be used to import external data defined with a set of points. This optimization approach could be employed as well to generate an initial blading geometry which could feed a more sophisticated optimization method based on a three-dimensional CFD solver.

An Efficient Chaotic Salp Swarm Optimization Approach Based on Ensemble Algorithm for Solving Class Imbalance Problem

2021 ◽  
Author(s):  
Rekha G ◽  
Krishna Reddy V ◽  
chandrashekar jatoth ◽  
Ugo Fiore
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Fitness Function ◽  
Class Imbalance ◽  
Optimization Approach ◽  
Class Imbalance Problem ◽  
Swarm Optimization ◽  
Adaboost Algorithm ◽  
Wide Range

Abstract Class imbalance problems have attracted the research community but a few works have focused on feature selection with imbalanced datasets. To handle class imbalance problems, we developed a novel fitness function for feature selection using the chaotic salp swarm optimization algorithm, an efficient meta-heuristic optimization algorithm that has been successfully used in a wide range of optimization problems. This paper proposes an Adaboost algorithm with chaotic salp swarm optimization. The most discriminating features are selected using salp swarm optimization and Adaboost classifiers are thereafter trained on the features selected. Experiments show the ability of the proposed technique to find the optimal features with performance maximization of Adaboost.

WINDOPT: An Optimization Tool for Floating Support Structures for Deep Water Wind Turbines

2011 ◽  
Author(s):  
Ivar Fylling ◽  
Petter Andreas Berthelsen
Keyword(s):  
Optimization Problems ◽  
Integrated Design ◽  
Design Tool ◽  
Mooring Line ◽  
Curvature Radius ◽  
Optimization Approach ◽  
Mooring System ◽  
Simple Relationship ◽  
Line Load ◽  
Geometrical Properties

An integrated design tool for optimization of a floating wind turbine support structure of the spar buoy type, including mooring system and power takeoff cable, is described in this paper. The program utilizes efficient design tools for analysis of mooring system forces and vessel motions, and combines this with a gradient method for solution of non-linear optimization problems with arbitrary constraints. The objective function to be minimized is the spar buoy cost, and the mooring line and cable costs. Typical design requirements that may be included as constraints are: mooring line load limitations and minimum fatigue life, cable curvature radius, cable tension, tower top acceleration, and vessel motion and inclination. The spar buoy is modelled as composed of a set of cylindrical sections with different mass, buoyancy and cost properties, where each section is assumed to have a uniform mass distribution. It is assumed that a representative initial cost figure is available, and that it can be scaled in proportion with material mass. A simple relationship between mass and geometrical properties is proposed for both massive and thin walled tubular sections. Examples are included to demonstrate the various aspects of the optimization approach, including different parameterizations of the spar buoy.

Study on Vibration Reduction Design of Steel-Composite Material Hybrid Mounting for Ship Based on Material Selection Optimization

2013 ◽  
Vol 694-697 ◽  
pp. 415-424
Author(s):  
Wei Wang ◽  
Lu Yun Chen ◽  
Yu Fang Zhang
Keyword(s):  
Power Flow ◽  
Optimization Problems ◽  
Material Selection ◽  
Vibration Reduction ◽  
Optimization Method ◽  
Optimization Approach ◽  
Structural Dynamic ◽  
Level Difference ◽  
Design Variables ◽  
Steel Composite

The material selection optimization for vibration reduction design is studied present article. By introducing the stacking sequence hypothesis of metal material, taking into account the power flow level difference and vibration level difference parameter, the mechanical parameters of the material and plies number are defined as design variables, and the mathematical model of structural dynamic optimization based on material selection optimization approach is established. Finally, a naval hybrid steel-composite mounting structure for example, by introducing genetic algorithm, the optimization problems is solved. The numerical results show that the optimization method is effective and feasible.

Multiobjective Optimization Using Fitness Function and Sequence Approximate Technology

2010 ◽  
Vol 139-141 ◽  
pp. 1192-1195
Author(s):  
Zhan Si Jiang ◽  
Hui Jiang
Keyword(s):  
Multiobjective Optimization ◽  
Pareto Front ◽  
Optimization Problems ◽  
Simulation Optimization ◽  
Fitness Function ◽  
Optimization Method ◽  
High Fidelity ◽  
Technology Research ◽  
Pareto Points ◽  
Multiobjective Optimization Problems

In order to obtain well-distributed Pareto front with less number of high-fidelity analysis during simulation optimization, a Pareto multiobjective optimization method is put forward by combining the fitness function and sequence approximate technology. Research shows that better Pareto points can be identified for the multiobjective optimization problems with convex, nonconvex or discontinue Pareto front, and the number of high-fidelity analysis can be largely reduced.

Optimized Radial Basis Function Metamodel for Expensive Engineering Design Optimization

2012 ◽  
Author(s):  
Lv Wang ◽  
Teng Long ◽  
Lei Peng ◽  
Li Liu
Keyword(s):  
Optimization Design ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimization Method ◽  
Kernel Functions ◽  
Optimization Approach ◽  
Engineering Optimization ◽  
Global Approximation ◽  
Approximation Accuracy ◽  
Engineering Optimization Problems

At the aim of alleviating the computational burden of complicated engineering optimization problems, metamodels have been widely employed to approximate the expensive blackbox functions. Among the popular metamodeling methods RBF metamodel well balances the global approximation accuracy, computational cost and implementation difficulty. However, the approximation accuracy of RBF metamodel is heavily influenced by the width factors of kernel functions, which are hard to determine and actually depend on the numerical behavior of expensive functions and distribution of samples. The main contribution of this paper is to propose an optimized RBF (ORBF) metamodel for the purpose of improving the global approximation capability with an affordable extra computational cost. Several numerical problems are used to compare the global approximation performance of the proposed ORBF metamodeling methods to determine the promising optimization approach. And the proposed ORBF is also adopted in adaptive metamodel-based optimization method. Two numerical benchmark examples and an I-beam optimization design are used to validate the adaptive metamodel-based optimization method using ORBF metamodel. It is demonstrated that ORBF metamodeling is beneficial to improving the optimization efficiency and global convergence capability for expensive engineering optimization problems.

scholarly journals Simulation and Optimization of Control of Selected Phases of Gyroplane Flight

2017 ◽  
Author(s):  
Wienczyslaw Stalewski
Keyword(s):  
Flight Control ◽  
Numerical Optimization ◽  
Stokes Equations ◽  
Optimization Problems ◽  
Computational Simulation ◽  
Optimization Methods ◽  
Optimization Method ◽  
Optimization Approach ◽  
Simulation And Optimization ◽  
Control Procedures

The optimization methods are increasingly used to solve challenging problems of aeronautical engineering. Typically, the optimization methods are utilized in design of aircraft airframe or its structure. The presented study is focused on an improvement of aircraft-flight-control procedures through the numerical optimization approach. The optimization problems concern selected phases of flight of light gyroplane - a rotorcraft using an unpowered rotor in autorotation to develop lift and an engine-powered propeller to provide thrust. An original methodology of computational simulation of rotorcraft flight was developed and implemented. In this approach the aircraft-motion equations are solved step-by-step, simultaneously with the solution of the Unsteady Reynolds-Averaged Navier-Stokes equations, which is conducted to assess aerodynamic forces acting on the aircraft. As a numerical optimization method, the BFGS algorithm was adapted. The developed methodology was applied to optimize the flight-control procedures in selected stages of gyroplane flight in direct proximity of the ground, where properly conducted control of the aircraft is critical to ensure flight safety and performance. The results of conducted computational optimizations proved qualitative correctness of the developed methodology. The research results can be helpful in design of easy-to-control gyroplanes and also in the training of pilots of this type of rotorcraft.

scholarly journals A Hybrid Global Optimization Method Based on Genetic Algorithm and Shrinking Box

2016 ◽  
Vol 10 (2) ◽  
pp. 67 ◽  
Author(s):  
Saleem Z. Ramadan
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Constrained Optimization ◽  
Penalty Function ◽  
Optimization Problems ◽  
Fitness Function ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Efficient Global Optimization ◽  
Global Optimization Method

<p class="zhengwen">This paper proposes a hybrid genetic algorithm method for optimizing constrained black box functions utilizing shrinking box and exterior penalty function methods (SBPGA). The constraints of the problem were incorporated in the fitness function of the genetic algorithm through the penalty function. The hybrid method used the proposed Variance-based crossover (VBC) and Arithmetic-based mutation (ABM) operators; moreover, immigration operator was also used. The box constraints constituted a hyperrectangle that kept shrinking adaptively in the light of the revealed information from the genetic algorithm about the optimal solution. The performance of the proposed algorithm was assessed using 11 problems which are used as benchmark problems in constrained optimization literatures. ANOVA along with a success rate performance index were used to analyze the model.</p>Based on the results, we believe that the proposed method is fairly robust and efficient global optimization method for Constrained Optimization Problems whether they are continuous or discrete.

The Application of the Combination of Monte Carlo Optimization Method based QSAR Modeling and Molecular Docking in Drug Design and Development

2020 ◽  
Vol 20 (14) ◽  
pp. 1389-1402 ◽  
Author(s):  
Maja Zivkovic ◽  
Marko Zlatanovic ◽  
Nevena Zlatanovic ◽  
Mladjan Golubović ◽  
Aleksandar M. Veselinović
Keyword(s):  
Monte Carlo ◽  
Molecular Docking ◽  
Computer Calculation ◽  
Molecular Graph ◽  
Optimization Method ◽  
Docking Studies ◽  
Optimization Approach ◽  
Qsar Modeling ◽  
Monte Carlo Optimization ◽  
Molecular Docking Studies

In recent years, one of the promising approaches in the QSAR modeling Monte Carlo optimization approach as conformation independent method, has emerged. Monte Carlo optimization has proven to be a valuable tool in chemoinformatics, and this review presents its application in drug discovery and design. In this review, the basic principles and important features of these methods are discussed as well as the advantages of conformation independent optimal descriptors developed from the molecular graph and the Simplified Molecular Input Line Entry System (SMILES) notation compared to commonly used descriptors in QSAR modeling. This review presents the summary of obtained results from Monte Carlo optimization-based QSAR modeling with the further addition of molecular docking studies applied for various pharmacologically important endpoints. SMILES notation based optimal descriptors, defined as molecular fragments, identified as main contributors to the increase/ decrease of biological activity, which are used further to design compounds with targeted activity based on computer calculation, are presented. In this mini-review, research papers in which molecular docking was applied as an additional method to design molecules to validate their activity further, are summarized. These papers present a very good correlation among results obtained from Monte Carlo optimization modeling and molecular docking studies.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

A microbial treatment population dynamics optimization approach

2021 ◽  
pp. 1-15
Author(s):  
Jinding Gao
Keyword(s):  
Population Dynamics ◽  
Information Exchange ◽  
Information Diffusion ◽  
Optimization Problems ◽  
Microbial Control ◽  
Function Optimization ◽  
Optimization Approach ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Number Of Individuals

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

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