Enriched optimization of molecular properties under constraints: an electrochromic example

2018 ◽  
Vol 3 (3) ◽  
pp. 485-495 ◽  
Author(s):  
B. Christopher Rinderspacher ◽  
Jennifer M. Elward
Keyword(s):  
Chemical Compound ◽  
Optimization Procedure ◽  
Search Space ◽  
Molecular Properties ◽  
Deterministic Optimization ◽  
The Given

We present a deterministic optimization procedure of molecular properties that ensures diverse coverage of the given chemical compound search space.

Smooth heuristic optimization on a complex chemical subspace

2015 ◽  
Vol 17 (37) ◽  
pp. 24322-24335 ◽  
Author(s):  
Jennifer M. Elward ◽  
B. Christopher Rinderspacher
Keyword(s):  
Chemical Compound ◽  
Heuristic Optimization ◽  
Deterministic Optimization ◽  
Complex Chemical ◽  
Reordering Algorithms

In the present work, several heuristic reordering algorithms for deterministic optimization on a combinatorial chemical compound space are evaluated for performance and efficiency.

Innovative Genetic Algorithmic Approach to Select Potential Patches Enclosing Real and Complex Zeros of Nonlinear Equation

2017 ◽  
Vol 6 (2) ◽  
pp. 18-37 ◽  
Author(s):  
Vijaya Lakshmi V. Nadimpalli ◽  
Rajeev Wankar ◽  
Raghavendra Rao Chillarige
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Equation ◽  
Complex Plane ◽  
Search Space ◽  
Regions Of Interest ◽  
Real Numbers ◽  
Algorithmic Approach ◽  
Space Reduction ◽  
Complex Zeros ◽  
The Given

In this article, an innovative Genetic Algorithm is proposed to find potential patches enclosing roots of real valued function f:R→R. As roots of f can be real as well as complex, the function is reframed on to complex plane by writing it as f(z). Thus, the problem now is transformed to finding potential patches (rectangles in C) enclosing z such that f(z)=0, which is resolved into two components as real and imaginary parts. The proposed GA generates two random populations of real numbers for the real and imaginary parts in the given regions of interest and no other initial guesses are needed. This is the prominent advantage of the method in contrast to various other methods. Additionally, the proposed ‘Refinement technique' aids in the exhaustive coverage of potential patches enclosing roots and reinforces the selected potential rectangles to be narrow, resulting in significant search space reduction. The method works efficiently even when the roots are closely packed. A set of benchmark functions are presented and the results show the effectiveness and robustness of the new method.

Assessment and Optimization of the Aerodynamic and Acoustic Characteristics of a Counter Rotating Open Rotor

2012 ◽  
Vol 134 (6) ◽  
Author(s):  
R. Schnell ◽  
J. Yin ◽  
C. Voss ◽  
E. Nicke
Keyword(s):  
Optimization Procedure ◽  
Operating Conditions ◽  
Far Field ◽  
Multi Objective Optimization ◽  
Automatic Optimization ◽  
Open Rotor ◽  
Acoustic Evaluation ◽  
Definition Of ◽  
Cfd Method ◽  
The Given

The present study demonstrates the aerodynamic and acoustic optimization potential of a counter rotating open rotor. The objective was to maximize the propeller efficiency at top of climb conditions and to minimize the noise emission at takeoff while fulfilling the given thrust specifications at two operating conditions (takeoff and top of climb) considered. Both objectives were successfully met by applying an efficient multi-objective optimization procedure in combination with a 3D RANS method. The acoustic evaluation was carried out with a coupled U-RANS and an analytic far field prediction method based on an integral Ffowcs Williams-Hawkings approach. This first part of the paper deals with the application of DLR’s CFD method TRACE to counter rotating open rotors. This study features the choice and placement of boundary conditions, resolution requirements, and a corresponding meshing strategy. The aerodynamic performance in terms of thrust, torque, and efficiency was evaluated based on steady state calculations with a mixing plane placed in between both rotors, which allowed for an efficient and reliable evaluation of the performance, in particular, within the automatic optimization. The aerodynamic optimization was carried by the application of AutoOpti, a multi-objective optimization procedure based on an evolutionary algorithm, which also was developed at the Institute of propulsion technology at DLR. The optimization presented in this paper features more than 1600 converged 3D steady-state CFD simulations at two operating conditions, takeoff and top of climb, respectively. In order to accelerate the optimization process, a surrogate model based on a Kriging interpolation on the response surfaces was introduced. The main constrains and regions of interest during the optimization were a given power split between the rotors at takeoff, retaining an axial outflow at the aft rotor exit at top of climb, and fulfilling the given thrust specifications at both operating conditions. Two objectives were defined: One was to maximize the (propeller) efficiency at top of climb conditions. The other objective was an acoustic criteria aiming at decreasing the rotor/rotor interaction noise at takeoff by smoothening the front rotor wakes. Approximately 100 geometric parameters were set free during the optimization to allow for a flexible definition of the 3D blade geometry in terms of rotor sweep, aft rotor clipping, hub contour as well as a flexible definition of different 2D profiles at different radial locations. The acoustic evaluation was carried out based on unsteady 3D-RANS computations with the same CFD method (TRACE) involving an efficient single-passage phase-lag approach. These unsteady results were coupled with the integral Ffowcs Williams-Hawkings method APSIM via a permeable control surface covering both rotors. The far field directivities and spectra for a linear microphone array were evaluated, here mainly at the takeoff certification point. This (still time consuming) acoustic evaluation was carried out after the automatic optimization for a few of the most promising individuals only, and results will be presented in comparison with the baseline configuration. This detailed acoustic evaluation also allowed for an assessment of the effectiveness of the acoustic cost function as introduced within the automatic optimization.

Experimental Improvement of Ejector Performance Through Numerical Optimization of Nozzle Geometry

2013 ◽  
Author(s):  
Navid Sharifi ◽  
Majid Sharifi
Keyword(s):  
Optimization Procedure ◽  
Nozzle Geometry ◽  
Cfd Models ◽  
Steam Ejector ◽  
Nominal Pressure ◽  
Fixed Constant ◽  
Nozzle Geometries ◽  
The One ◽  
Suction Flow ◽  
The Given

Ejectors are widely used in different applications such as refrigeration, propulsion, evacuation and aerospace. They use a pressurized flow as a motive stream to entrain a secondary flow or suction flow. In the current study, a malfunctioning steam ejector is studied experimentally to identify the sources of low compression ratio. This ejector was designed to operate under a motive pressure of 6 bar. However, the required vacuum in the system was not attained unless the pressure of motive steam was increased to 8 bar. The steam ejector was coupled with other unit operating facilities and hence, the ejector replacement was very costly. Therefore, the fastest and the most inexpensive way of improving the device performance was considered as replacing just the primary nozzle and without any further change in ejector’s geometry. To achieve the required vacuum under the available motive pressure (i.e. 6 bar), a CFD–based optimization procedure was performed and different nozzle shapes were numerically investigated. The CFD Models were constrained to a fixed constant throat since the optimized nozzle shall not consume more flow rate than the former one. Ten different nozzle geometries were scrutinized in this numerical simulation and the one, which yields the highest entraining performance under the given boundary condition (i.e. motive flow pressure of 6 bar), was selected as the most optimized nozzle and manufactured. After installing the designed nozzle, an improved entrainment capability was observed and a desired vacuum level was attained under the nominal pressure of 6 bar.

Performance Analysis of a V2G Brokering Agent

2014 ◽  
Vol 686 ◽  
pp. 634-638 ◽  
Author(s):  
Jung Hoon Lee ◽  
Ji Hyun Kang
Keyword(s):  
Search Space ◽  
Stored Energy ◽  
Demand And Supply ◽  
Vehicle To Grid ◽  
Consumption Ratio ◽  
Available Energy ◽  
Power Load ◽  
Stay Time ◽  
Temporal And Spatial ◽  
The Given

This paper first presents a brokering architecture for a vehicle-to-grid electricity trades between electric vehicles and a microgrid, and then measures its performance, particularly focusing on the stay time, which significantly affects the scheduling flexibility. The brokering service matches demand and supply on battery-stored energy, traversing the search space to find an energy allocation for each time slot. The slot-by-slot schedule, generated from the two-way interaction protocol, coordinates the arrival time of each seller at the microgrid, achieving temporal and spatial power load shift. The performance measurement based on a prototype implementation analyzes the effect on the lacking and surplus energy, the demand meet ratio, and the effective consumption ratio. The experiment result shows that the brokering scheme can fully take advantage of enhanced flexibility in placing available energy on the time slots, reducing the lacking amount by up to 38.4 % as well as enhancing the consumption ratio by up to 27 % for the given parameter set.

scholarly journals Decoding Methods in Neural Language Generation: A Survey

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 355
Author(s):  
Sina Zarrieß ◽  
Henrik Voigt ◽  
Simeon Schüz
Keyword(s):  
Neural Network ◽  
Search Space ◽  
Output Sequence ◽  
Language Generation ◽  
Survey Paper ◽  
Considerable Impact ◽  
Systematic Understanding ◽  
The Given ◽  
Broad Type ◽  
And Task

Neural encoder-decoder models for language generation can be trained to predict words directly from linguistic or non-linguistic inputs. When generating with these so-called end-to-end models, however, the NLG system needs an additional decoding procedure that determines the output sequence, given the infinite search space over potential sequences that could be generated with the given vocabulary. This survey paper provides an overview of the different ways of implementing decoding on top of neural network-based generation models. Research into decoding has become a real trend in the area of neural language generation, and numerous recent papers have shown that the choice of decoding method has a considerable impact on the quality and various linguistic properties of the generation output of a neural NLG system. This survey aims to contribute to a more systematic understanding of decoding methods across different areas of neural NLG. We group the reviewed methods with respect to the broad type of objective that they optimize in the generation of the sequence—likelihood, diversity, and task-specific linguistic constraints or goals—and discuss their respective strengths and weaknesses.

Innovative Genetic Algorithmic Approach to Select Potential Patches Enclosing Real and Complex Zeros of Nonlinear Equation

2021 ◽  
pp. 223-243
Author(s):  
Vijaya Lakshmi V. Nadimpalli ◽  
Rajeev Wankar ◽  
Raghavendra Rao Chillarige
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Equation ◽  
Search Space ◽  
Regions Of Interest ◽  
New Method ◽  
Real Numbers ◽  
Algorithmic Approach ◽  
Space Reduction ◽  
Complex Zeros ◽  
The Given

In this article, an innovative Genetic Algorithm is proposed to find potential patches enclosing roots of real valued function f:R→R. As roots of f can be real as well as complex, the function is reframed on to complex plane by writing it as f(z). Thus, the problem now is transformed to finding potential patches (rectangles in C) enclosing z such that f(z)=0, which is resolved into two components as real and imaginary parts. The proposed GA generates two random populations of real numbers for the real and imaginary parts in the given regions of interest and no other initial guesses are needed. This is the prominent advantage of the method in contrast to various other methods. Additionally, the proposed ‘Refinement technique' aids in the exhaustive coverage of potential patches enclosing roots and reinforces the selected potential rectangles to be narrow, resulting in significant search space reduction. The method works efficiently even when the roots are closely packed. A set of benchmark functions are presented and the results show the effectiveness and robustness of the new method.

Numerical Optimization Using the Heart Algorithm

2018 ◽  
Vol 9 (2) ◽  
pp. 33-37
Author(s):  
Abdolreza Hatamlou
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Heart Function ◽  
Circulatory System ◽  
Optimal Solution ◽  
Search Space ◽  
Human Beings ◽  
Unconstrained Optimization Problems ◽  
And Performance ◽  
The Given

In this article the authors investigate the application of the heart algorithm for solving unconstraint numerical optimization problems. Heart algorithms are a novel optimization algorithm which mimics the heart function and circulatory system procedure in the human beings. It starts with a number of candidate solutions for the given problem and utilizes the contraction and expansion actions to move the candidates in the search space for finding optimal solution. The applicability and performance of the heart algorithm for solving unconstrained optimization problems has been tested using several benchmark functions. Experimental results show its potential and superiority.

scholarly journals A biologically inspired approach to feasible gait learning for a hexapod robot

2010 ◽  
Vol 20 (1) ◽  
pp. 69-84 ◽  
Author(s):  
Dominik Belter ◽  
Piotr Skrzypczyński
Keyword(s):  
Fitness Function ◽  
Search Space ◽  
Walking Robots ◽  
Hexapod Robot ◽  
Walking Robot ◽  
Insect Behaviour ◽  
Biologically Inspired ◽  
Gait Patterns ◽  
Gait Learning ◽  
The Given

A biologically inspired approach to feasible gait learning for a hexapod robotThe objective of this paper is to develop feasible gait patterns that could be used to control a real hexapod walking robot. These gaits should enable the fastest movement that is possible with the given robot's mechanics and drives on a flat terrain. Biological inspirations are commonly used in the design of walking robots and their control algorithms. However, legged robots differ significantly from their biological counterparts. Hence we believe that gait patterns should be learned using the robot or its simulation model rather than copied from insect behaviour. However, as we have foundtahula rasalearning ineffective in this case due to the large and complicated search space, we adopt a different strategy: in a series of simulations we show how a progressive reduction of the permissible search space for the leg movements leads to the evolution of effective gait patterns. This strategy enables the evolutionary algorithm to discover proper leg co-ordination rules for a hexapod robot, using only simple dependencies between the states of the legs and a simple fitness function. The dependencies used are inspired by typical insect behaviour, although we show that all the introduced rules emerge also naturally in the evolved gait patterns. Finally, the gaits evolved in simulations are shown to be effective in experiments on a real walking robot.

TRANSLATION-, ROTATION- AND SCALE- INVARIANT RECOGNITION OF HAND-DRAWN SYMBOLS IN SCHEMATIC DIAGRAMS

1990 ◽  
Vol 04 (01) ◽  
pp. 1-25 ◽  
Author(s):  
SEONG-WHAN LEE ◽  
JIN H. KIM ◽  
FRANS C.A. GROEN
Keyword(s):  
Minimum Distance ◽  
Early Stage ◽  
Search Space ◽  
Distance Measures ◽  
Scale Invariant ◽  
Attributed Graph ◽  
Geometrical Constraints ◽  
The Given ◽  
Single Pixel ◽  
Schematic Diagrams

In this paper, a model-based scheme for recognizing hand-drawn symbols in schematic diagrams using attributed graph (AG) matching in the absence of any information concerning their pose (translation, rotation and scale) is described. The process of AG matching proceeds as follows. First, an observed AG (AGO) is constructed from single-pixel-width line-representation of an observed symbol. Second, the pose of the AGO is estimated in terms of translation, rotation and scale with respect to the model AGs (AGM s ). The search space is effectively pruned by introducing the concept of control vertex and applying geometrical constraints in an early stage. In this step, a small number of candidate AGM s are selected. Third, correspondences between components of the observed AG after normalization (AGON) and those of the AGMs are found for the given poses. Fourth, distance measures between the AGON and the AGM s are calculated, based upon the correspondences. Finally, the AGON is classified as the AGM with the minimum distance. Experimental results for hand-drawn symbols with and without templates show that using AG matching is very efficient and successful for translation-, rotation- and scale-invariant recognition of hand-drawn symbols in schematic diagrams.

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