A competitive-cooperative co-evolutionary optimization algorithm based on cloud model

2011 ◽  
Author(s):  
Wei Li ◽  
Lei Wang
Keyword(s):  
Optimization Algorithm ◽  
Cloud Model ◽  
Evolutionary Optimization

A New Approach to Compensate Oscillations of Path-Linked Presses Caused by Inertial Forces

2016 ◽  
Vol 1140 ◽  
pp. 361-368
Author(s):  
Stefan Hilscher ◽  
Richard Krimm ◽  
Bernd Arno Behrens
Keyword(s):  
Conceptual Design ◽  
Optimization Algorithm ◽  
Evolutionary Optimization ◽  
Inertial Forces ◽  
New Approach ◽  
Virtual Testing ◽  
Linear Motors ◽  
Control Signals ◽  
The Press ◽  
Mass Balancing

Presses with mechanical linkages based on levers between motor and ram (path-linked presses) tend to oscillate due to inertial forces as a consequence of the drive parts motion.In this publication a new approach for a mass-balancing system is presented. This system allows to generate the optimal compensation forces needed to counteract the inertial forces by means of four linear motors. The control signals for the linear motors are specified by an evolutionary optimization algorithm, which operates on the base of measured accelerations of the press frame. The control signals of the linear motors are created in a way that the machines oscillations are reduced to a minimum. This way the presented mass-balancing system adapts itself automatically to varying conditions during the operation of the machine, such as a tool change or a varying stroke rate.In particular, the present publication provides the results of the conceptual design and the virtual testing of this approach, which has been mainly carried out with the help of multiple-body simulations.

S.T. (Shafiabady-Teshnehlab) Evolutionary Optimization Algorithm

2011 ◽  
Vol 2 (3) ◽  
pp. 73-82
Author(s):  
Niusha Shafiabady ◽  
Mohammad Teshnehlab ◽  
Mohammad Ali Nekoui
Keyword(s):  
Optimization Algorithm ◽  
Evolutionary Optimization

Cloud Adapt Particle Swarm Optimization Algorithm for Distribution Network Planning with Distributed Generation

2013 ◽  
Vol 427-429 ◽  
pp. 1136-1140 ◽  
Author(s):  
Bu Quan Xu ◽  
Li Chen Zhang ◽  
Bu Zhen Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Generation ◽  
Optimization Algorithm ◽  
Distribution Systems ◽  
Particle Swarm Optimization Algorithm ◽  
Cloud Model ◽  
Particle Swarm ◽  
Value Assessment ◽  
General Group ◽  
Swarm Optimization

Distributed Generation (DG) can be used to improve power quality, power supply reliability and reduce network loss et. Meanwhile Particle Swarm Optimization algorithm (PSO) is easy to fall into the local minimum. In this paper we propose a Cloud Adaptive Particle Swarm Optimization algorithm (CAPSO) to optimize the site and size of DG based on cloud model which has a tendency and randomness property. Judged by two dynamic value assessment, particle belongs to which group, excellent, general and poor. The inertia weight in general group is adaptively varied depending on X-conditional cloud generation. Taking the minimum network loss as the objective function, simulation on the IEEE 33BUS distribution systems to validate the methodology. Analysis and simulations indicate that it has good convergence speed and exactness.

Performance of a bell-curve based evolutionary optimization algorithm

2000 ◽  
Author(s):  
Rex Kincaid ◽  
Michael Weber ◽  
Jaroslaw Sobieszczanski-Sobieski
Keyword(s):  
Optimization Algorithm ◽  
Evolutionary Optimization ◽  
Bell Curve
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