A new constrained optimization evolutionary algorithm by using good point set

2007 ◽  
Author(s):  
Hui Liu ◽  
Zixing Cai ◽  
Yong Wang
Keyword(s):  
Constrained Optimization ◽  
Evolutionary Algorithm ◽  
Good Point ◽  
Point Set ◽  
Good Point Set

Modified K-means clustering algorithm based on good point set and Leader method

2011 ◽  
Vol 31 (5) ◽  
pp. 1359-1362
Author(s):  
Yan-ping ZHANG ◽  
Juan ZHANG ◽  
Cheng-gang HE ◽  
Wei-cui CHU ◽  
Li-na ZHANG
Keyword(s):  
Clustering Algorithm ◽  
Good Point ◽  
Point Set ◽  
Good Point Set

scholarly journals Research on Financial Early-warning based on GIHS Improved BP_AdaBoost Algorithm

2018 ◽  
Vol 173 ◽  
pp. 03004
Author(s):  
Gui-fang Shen ◽  
Yi-Wen Zhang
Keyword(s):  
Neural Network ◽  
Ensemble Learning ◽  
Early Warning ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Adaboost Algorithm ◽  
Good Point ◽  
Ensemble Learning Algorithm ◽  
Point Set ◽  
Good Point Set

To improve the accuracy of the financial early warning of the company, aiming at defects of slow learning speed, trapped in local solution and inaccurate operating result of the traditional BP neural network with random initial weights and thresholds, a parallel ensemble learning algorithm based on improved harmony search algorithm using good point set (GIHS) optimize the BP_Adaboost is proposed. Firstly, the good-point set is used to construct a more high quality initial harmony library, and it adjusts the parameters dynamically during the search process and generates several solutions in each iteration so as to make full use of information of harmony memory to improve the global search ability and convergence speed of algorithm. Secondly, ten financial indicators are chosen as the inputs of BP neural network value, and GIHS algorithm and BP neural network are combined to construct the parallel ensemble learning algorithm to optimize BP neural network initial weights value and output threshold value. Finally, many of these weak classifier is composed as strong classifier through the AdaBoost algorithm. The improved algorithm is validated in the company's financial early warning. Simulation results show that the performance of GIHS algorithm is better than the basic HS and IHS algorithm, and the GIHS-BP_AdaBoost classifier has higher classification and prediction accuracy.

scholarly journals Method for Measurement Uncertainty Evaluation of Cylindricity Error Based on Good Point Set

Procedia CIRP ◽  
2018 ◽  
Vol 75 ◽  
pp. 373-378
Author(s):  
Ren Lifei ◽  
Liu Ting ◽  
Zhao Qijian ◽  
Yang Jiangxin ◽  
Cao Yanlong
Keyword(s):  
Measurement Uncertainty ◽  
Uncertainty Evaluation ◽  
Good Point ◽  
Point Set ◽  
Cylindricity Error ◽  
Measurement Uncertainty Evaluation ◽  
Good Point Set

A New Particle Swarm Algorithm for Solving Constrained Optimization Problems

2013 ◽  
Vol 734-737 ◽  
pp. 2875-2879
Author(s):  
Tie Bin Wu ◽  
Yun Cheng ◽  
Yun Lian Liu ◽  
Tao Yun Zhou ◽  
Xin Jun Li
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Constrained Optimization Problems ◽  
Particle Population ◽  
Point Set ◽  
Good Point Set ◽  
Improved Pso

Considering that the particle swarm optimization (PSO) algorithm has a tendency to get stuck at the local solutions, an improved PSO algorithm is proposed in this paper to solve constrained optimization problems. In this algorithm, the initial particle population is generated using good point set method such that the initial particles are uniformly distributed in the optimization domain. Then, during the optimization process, the particle population is divided into two sub-populations including feasible sub-population and infeasible sub-population. Finally, different crossover operations and mutation operations are applied for updating the particles in each of the two sub-populations. The effectiveness of the improved PSO algorithm is demonstrated on three benchmark functions.

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