scholarly journals Multiple R&D Projects Scheduling Optimization with Improved Particle Swarm Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Mengqi Liu ◽  
Miyuan Shan ◽  
Juan Wu
Keyword(s):  
Project Scheduling ◽  
Simulation Experiment ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Scheduling Optimization ◽  
Scheduling Model ◽  
Make To Order ◽  
Swarm Algorithm ◽  
The One ◽  
Fierce Competition

For most enterprises, in order to win the initiative in the fierce competition of market, a key step is to improve their R&D ability to meet the various demands of customers more timely and less costly. This paper discusses the features of multiple R&D environments in large make-to-order enterprises under constrained human resource and budget, and puts forward a multi-project scheduling model during a certain period. Furthermore, we make some improvements to existed particle swarm algorithm and apply the one developed here to the resource-constrained multi-project scheduling model for a simulation experiment. Simultaneously, the feasibility of model and the validity of algorithm are proved in the experiment.

Improved Collaborative Particle Swarm Algorithm for Job Shop Scheduling Optimization

2011 ◽  
Vol 4 (6) ◽  
pp. 2180-2183 ◽  
Author(s):  
Aijun Liu ◽  
Yu Yang ◽  
Qingsong Xing ◽  
Hao Yao ◽  
Yudong Zhang ◽  
...  
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Scheduling Optimization ◽  
Shop Scheduling ◽  
Swarm Algorithm

scholarly journals Image Resteoration by BP Neural Based on PSO

2018 ◽  
Vol 36 (4) ◽  
pp. 709-714 ◽  
Author(s):  
Wenzhong Wang ◽  
Shusheng Zhang ◽  
Suihuai Yu
Keyword(s):  
Optimization Algorithm ◽  
Particle Swarm ◽  
Training Sample ◽  
Bp Algorithm ◽  
Algorithm Analysis ◽  
Particle Swarm Algorithm ◽  
Neural Network Algorithm ◽  
Swarm Algorithm ◽  
The One ◽  
Better Than

Based on PSO-BP algorithm combining particle swarm algorithm with BP neural network algorithm, this paper applies this algorithm to image restoration based on optimization. In the PSO-BP optimization algorithm model, on the one hand, the error of each training sample of BP algorithm is reversed, and the original image is used as the reference to modify the weight threshold of BP algorithm. On the other hand, it is optimized by forward particle swarm algorithm and BP algorithm. Finally, through the algorithm analysis and experimental data, the recovery effect of PSO-BP optimization algorithm is better than that of the same type algorithm.

Dynamic simulation of wind field in three-dimensional display space considering particle swarm algorithm fuzzy logic

2021 ◽  
pp. 1-6
Author(s):  
Honglei Xu ◽  
Linhuan Wang
Keyword(s):  
Fuzzy Logic ◽  
Dynamic Simulation ◽  
Wind Field ◽  
Three Dimensional ◽  
Measurement Data ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Three Dimensional Display ◽  
Swarm Algorithm ◽  
Synchronous Measurement

In order to improve the accuracy of dynamic detection of wind field in the three-dimensional display space, system software is carried out on the actual scene and corresponding airborne radar observation information data, and the particle swarm algorithm fuzzy logic algorithm is introduced into the wind field dynamic simulation process in three-dimensional display space, to analyze the error of the filtering result in detail, to process the hurricane Lily Doppler radar measurement data with the optimal adaptive filtering according to the error data. The three-dimensional wind field synchronous measurement data obtained by filtering was compared with three-dimensional wind field synchronous measurement data of the GPS dropsonde in this experiment, the sea surface wind field measurement data of the multi-band microwave radiometer, and the wind field data at aircraft altitude.

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