scholarly journals Pool-BCGA: a parallelised generation-free genetic algorithm for the ab initio global optimisation of nanoalloy clusters

2015 ◽  
Vol 17 (3) ◽  
pp. 2104-2112 ◽  
Author(s):  
A. Shayeghi ◽  
D. Götz ◽  
J. B. A. Davis ◽  
R. Schäfer ◽  
R. L. Johnston
Keyword(s):  
Genetic Algorithm ◽  
Ab Initio ◽  
Global Optimisation

A new parallelised generation free implementation of the Birmingham cluster genetic algorithm is presented for the efficient global optimisation of nanoalloy clusters.

Tuning PID Controller Using Genetic Algorithms for Electro-Hydraulic System with Tracking Force Control

2014 ◽  
Vol 931-932 ◽  
pp. 1318-1322 ◽  
Author(s):  
Pisan Moonumca ◽  
Nattawoot Depaiwa ◽  
Yoshio Yamamoto
Keyword(s):  
Genetic Algorithm ◽  
Force Control ◽  
Hydraulic System ◽  
Pid Controller ◽  
Controller Design ◽  
Input Function ◽  
Tracking Force ◽  
Sine Signal ◽  
Tuning Methods ◽  
The Mathematical Model

The aim of this paper is design a controller for force control of an Electro-Hydraulic System and selection of a PID gains are using genetic algorithm. The mathematical model is considered as the Newton second law and compressible fluid flow theory. This paper compares two kinds of tuning methods for PID controller. One is the controller design by the genetic algorithm, second is the controller design by the Ziegler and Nichols method. Each PID gains are tested with step input function, square signal, half-sine signal and half-saw signal. And results show performance index with rise time, settling time and Maximum %OS. It was found that the proposed PID gains tuning by the genetic algorithm is better than the Ziegler & Nichols method.

scholarly journals The Birmingham parallel genetic algorithm and its application to the direct DFT global optimisation of IrN(N = 10–20) clusters

Nanoscale ◽  
2015 ◽  
Vol 7 (33) ◽  
pp. 14032-14038 ◽  
Author(s):  
Jack B. A. Davis ◽  
Armin Shayeghi ◽  
Sarah L. Horswell ◽  
Roy L. Johnston
Keyword(s):  
Genetic Algorithm ◽  
Global Optimisation ◽  
Parallel Genetic Algorithm

scholarly journals GIGA: a versatile genetic algorithm for free and supported clusters and nanoparticles in the presence of ligands

Nanoscale ◽  
2019 ◽  
Vol 11 (18) ◽  
pp. 9042-9052 ◽  
Author(s):  
Marc Jäger ◽  
Rolf Schäfer ◽  
Roy L. Johnston
Keyword(s):  
Genetic Algorithm ◽  
Gas Phase ◽  
Molecular Species ◽  
First Principles ◽  
Global Optimisation ◽  
Supported Clusters

We present a versatile parallelised genetic algorithm, which is able to perform global optimisation from first principles for pure and mixed free clusters in the gas phase, supported on surfaces or in the presence of one or several atomic or molecular species (ligands or adsorbates).

Parameter Optimization in the Simulation Package of Hydraulic System

2013 ◽  
Vol 645 ◽  
pp. 184-187
Author(s):  
Li Xu ◽  
Qian Qian Lu ◽  
Wei Shao
Keyword(s):  
Genetic Algorithm ◽  
Injection Molding ◽  
Parameter Optimization ◽  
Hydraulic System ◽  
System Simulation ◽  
Hydraulic Parameters ◽  
Molding Machine ◽  
Design Cycle ◽  
Simulation And Experiment ◽  
Simulation Package

The paper discusses the optimization technology and algorithm features in the hydraulic system simulation. It presents the realization in the package. Detailed description of the principle and development is shown, focused on parameter optimization by parameter error integration and genetic algorithm which are realized in the optimization module. The application is an example of hydraulic system of injection molding machine which showed the efficiency and capacity of the module. The comparison between curves of simulation and experiment indicated the achievements, to find optimized hydraulic parameters efficiently and quickly so as to shorten the design cycle.

Optimization of the Hydraulic Control System Utilizing BP Neural Network Control Strategy Based on Genetic Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 1245-1252
Author(s):  
Ying Ying Feng ◽  
Nan Mu Hui ◽  
Zong An Luo ◽  
Dian Hua Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Control ◽  
Hydraulic System ◽  
Learning Ability ◽  
Hydraulic Control ◽  
Control Effect ◽  
Hydraulic Control System

For the characteristic of the MMS series Thermo-Mechanical Simulator hydraulic control system, using traditional PID control method can not achieve the desired control effect. Basing on genetic algorithm, BP neural network, which has the arbitrary non-linear approximation ability, self-learning ability and generalization ability, has been used into the hydraulic control system to achieve the online adjustment of the weighting coefficients and the adaptive adjustment of PID control parameters. The results of simulation and online tests show that the control effect of hydraulic system has been improved significantly, and the accurate control of hydraulic system hammer displacement has been realized.

scholarly journals PdnAg(4−n) and PdnPt(4−n) clusters on MgO (100): a density functional surface genetic algorithm investigation

Nanoscale ◽  
2014 ◽  
Vol 6 (20) ◽  
pp. 11777-11788 ◽  
Author(s):  
Christopher J. Heard ◽  
Sven Heiles ◽  
Stefan Vajda ◽  
Roy L. Johnston
Keyword(s):  
Genetic Algorithm ◽  
Noble Metal ◽  
Density Functional ◽  
Global Optimisation ◽  
Bimetallic Clusters ◽  
Functional Surface ◽  
Mgo Substrate

Global optimisation of catalytically relevant noble metal mono and bimetallic clusters is performed directly on an MgO substrate with DFT. Charge is distributed locally upon the cluster, providing a means to atomically control binding and reaction sites, as found for CO molecules on Pd/Ag/Pt.

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