A New Approach of the Rain-Fall Optimization Algorithm Using Parallelization

2020 ◽  
pp. 13-26
Author(s):  
Juan Manuel Guerrero-Valadez ◽  
Felix Martinez-Rios
Keyword(s):  
Optimization Algorithm ◽  
New Approach ◽  
Rain Fall

Visualization Service Model for Job Scheduling

2011 ◽  
Vol 282-283 ◽  
pp. 683-686
Author(s):  
Yong Ling Yu ◽  
Tao Guan ◽  
Jin Fa Shi
Keyword(s):  
Enterprise Resource Planning ◽  
Optimization Algorithm ◽  
Optimization Model ◽  
Job Scheduling ◽  
Resource Planning ◽  
Information Services ◽  
Production Data ◽  
Service Model ◽  
New Approach ◽  
Manufacturing Information

With the expansion of manufacturing information services, visualization service in enterprise resource planning has recently become a hot topic. In this paper, we propose a new approach for job scheduling visualization service and a model of visualization service constructed from the share of production data and job scheduling information. Moreover, the optimization model of visualization service is given. At last, we discuss the optimization algorithm for this model.

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.

scholarly journals Shrinkage of Real Power Loss by Enriched Brain Storm Optimization Algorithm

2019 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Human Being ◽  
New Approach ◽  
Real Power ◽  
Brain Storm Optimization ◽  
Course Of Action ◽  
Simulation Results ◽  
Power Problem

<p class="Author">This paper proposes Enriched Brain Storm Optimization (EBSO) algorithm is used for soving reactive power problem. Human being are the most intellectual creature in this world. Unsurprisingly, optimization algorithm stimulated by human being inspired problem solving procedure should be advanced than the optimization algorithms enthused by collective deeds of ants, bee, etc. In this paper, we commence a new Enriched brain storm optimization algorithm, which was enthused by the human brainstorming course of action. In the projected Enriched Brain Storm Optimization (EBSO) algorithm, the vibrant clustering strategy is used to perk up the k-means clustering process. The most important view of the vibrant clustering strategy is that; regularly execute the k-means clustering after a definite number of generations, so that the swapping of information wrap all ideas in the clusters to accomplish suitable searching capability. This new approach leads to wonderful results with little computational efforts. In order to evaluate the efficiency of the proposed Enriched Brain Storm Optimization (EBSO) algorithm, has been tested standard IEEE 118 &amp; practical 191 bus test systems and compared to other standard reported algorithms. Simulation results show that Enriched Brain Storm Optimization (EBSO) algorithm is superior to other algorithms in reducing the real power loss.</p>

scholarly journals The multilevel ant stigmergy algorithm for numerical optimization

2006 ◽  
Vol 19 (2) ◽  
pp. 247-260 ◽  
Author(s):  
Peter Korosec ◽  
Jurij Silc
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Numerical Optimization ◽  
Optimization Techniques ◽  
Industrial Case Study ◽  
New Approach ◽  
Collective Work ◽  
Better Than

The Multilevel Ant Stigmergy Algorithm (MASA) is a new approach to solving multi-parameter problems based on stigmergy, a type of collective work that can be observed in nature. In this paper we evaluate the performance of MASA regarding its applicability as numerical optimization techniques. The evaluation is performed with several widely used benchmarks functions, as well as on an industrial case study. We also compare the MASA with Differential Evolution, well-known numerical optimization algorithm. The average solution obtained with the MASA was better than a solution recently found using Differential Evolution.

scholarly journals Improved Estimates of the Vertical Structures of Rain Using Single Frequency Doppler Radars

2021 ◽  
Author(s):  
A. R. Jameson ◽  
Michael Larsen ◽  
David Wolff
Keyword(s):  
Drop Size ◽  
Physical Structure ◽  
Single Frequency ◽  
Fall Rate ◽  
Size Distributions ◽  
New Approach ◽  
Rain Fall ◽  
Temporal And Spatial ◽  
Air Motion ◽  
Drop Size Distributions

It is important to understand the statistical-physical structure of the rain in the vertical so that observations aloft can be translated meaningfully into what will occur at the surface. In order to achieve this understanding, it is necessary to gather high temporal and spatial resolution observations of rain in the vertical. This can only be accomplished using radars. It can be achieved by translating radar Doppler spectra into drop size distributions which can then be integrated to calculate variables such as the rain fall rate. A long-standing difficulty in using such measurements, however, is the problem of vertical air motion which can shift the Doppler spectra, and, therefore, significantly alter the deduced drop size distributions and integrated variables. In this work, we illustrate the improvement in measured rain structures using a new approach for removing the effect of mean vertical air motion. It is demonstrated that the new approach proposed here not only produces what appear to be better estimates of the rainfall rates, but, also as a consequence, provides estimates of the temporal and spatial regionally coherent updraft and downdrafts occurring in the precipitation. Furthermore, the technique is readily applicable to other radars especially those operating at non-attenuating frequencies.

scholarly journals An α-Model Parametrization Algorithm for Optimization with Differential-Algebraic Equations

2022 ◽  
Vol 12 (2) ◽  
pp. 890
Author(s):  
Paweł Dra̧g
Keyword(s):  
Optimization Algorithm ◽  
Numerical Optimization ◽  
Differential Algebraic Equations ◽  
Multiple Shooting ◽  
Small Range ◽  
Algebraic Equations ◽  
New Approach ◽  
Highly Nonlinear ◽  
Special Cases ◽  
Wide Range

An optimization task with nonlinear differential-algebraic equations (DAEs) was approached. In special cases in heat and mass transfer engineering, a classical direct shooting approach cannot provide a solution of the DAE system, even in a relatively small range. Moreover, available computational procedures for numerical optimization, as well as differential- algebraic systems solvers are characterized by their limitations, such as the problem scale, for which the algorithms can work efficiently, and requirements for appropriate initial conditions. Therefore, an αDAE model optimization algorithm based on an α-model parametrization approach was designed and implemented. The main steps of the proposed methodology are: (1) task discretization by a multiple-shooting approach, (2) the design of an α-parametrized system of the differential-algebraic model, and (3) the numerical optimization of the α-parametrized system. The computations can be performed by a chosen iterative optimization algorithm, which can cooperate with an outer numerical procedure for solving DAE systems. The implemented algorithm was applied to solve a counter-flow exchanger design task, which was modeled by the highly nonlinear differential-algebraic equations. Finally, the new approach enabled the numerical simulations for the higher values of parameters denoting the rate of changes in the state variables of the system. The new approach can carry out accurate simulation tests for systems operating in a wide range of configurations and created from new materials.

scholarly journals REDUCTION OF TRUE POWER LOSS BY IMPROVED ALGORITHM

2018 ◽  
Vol 6 (8) ◽  
pp. 105-113
Author(s):  
K. Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Test System ◽  
New Approach ◽  
Real Power ◽  
Brain Storm Optimization ◽  
Course Of Action ◽  
Simulation Results ◽  
Power Problem

This paper proposes Improved Brain Storm Optimization (IBSO) algorithm is used for solving reactive power problem. predictably, optimization algorithm stimulated by human being inspired problem-solving procedure should be highly developed than the optimization algorithms enthused by collective deeds of ants, bee, etc. In this paper, a new Improved brain storm optimization algorithm defined, which was stimulated by the human brainstorming course of action. In the projected Improved Brain Storm Optimization (IBSO) algorithm, the vibrant clustering strategy is used to perk up the k-means clustering process & exchange of information wrap all ideas in the clusters to accomplish suitable searching capability. This new approach leads to wonderful results with little computational efforts. In order to evaluate the efficiency of the proposed Improved Brain Storm Optimization (IBSO) algorithm, has been tested standard IEEE 30 bus test system and compared to other standard reported algorithms. Simulation results show that Improved Brain Storm Optimization (IBSO) algorithm is superior to other algorithms in reducing the real power loss.

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