A Hybrid Genetic Algorithm for Structural Optimization with Discrete Variables

2011 ◽  
Author(s):  
Pengfei Guo ◽  
Xuezhi Wang ◽  
Yingshi Han
Keyword(s):  
Genetic Algorithm ◽  
Structural Optimization ◽  
Hybrid Genetic Algorithm ◽  
Discrete Variables

Improved Genetic Algorithms Based Optimal Design of The Shuangji River Aqueduct

2011 ◽  
Vol 201-203 ◽  
pp. 1288-1291
Author(s):  
Xin Li Bai ◽  
Wei Yu ◽  
Dan Fei Wang ◽  
Yuan Yuan Fan
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Structural Optimization ◽  
Large Scale ◽  
Hybrid Genetic Algorithm ◽  
Computer Software ◽  
Search Method ◽  
Discrete Variables ◽  
Original Design ◽  
Design Plan

The simple genetic algorithm (SGA) is taken as the global search method, and the traditional direct search method for mixed-discrete variables as the local search method. The improved (hybrid) genetic algorithm (IGA) is obtained by improving the SGA. And through the introduction of penalty constraints, the problem dealing with the constraints in GA is successfully resolved. A mathematical model for structural optimization of aqueduct is established, and computer software is developed for structural optimization of large-scale aqueduct based on IGA. Using this program, the Shuangji River aqueduct is optimized and Rectangle-sectioned aqueduct design plan is obtained. Compared with the original design plan, optimal design is very economical and was adopted by Design Institute.

Application of Improved Combined Shape Genetic Algorithm on Structural Optimization

2014 ◽  
Vol 602-605 ◽  
pp. 3119-3122
Author(s):  
Jun Xie ◽  
Jie Yan ◽  
Jing Yu Zhang ◽  
Yong Feng Xu ◽  
Meng Chen
Keyword(s):  
Genetic Algorithm ◽  
Structural Optimization ◽  
Optimization Problems ◽  
Design Method ◽  
Hybrid Genetic Algorithm ◽  
Initial Point ◽  
Shape Operator ◽  
Constrained Optimization Problems ◽  
New Approach ◽  
Optimal Design Method

A new approach to the generation of an initial point is proposed for discrete combined shape, which improves fully the local searching capability of discrete combined shape algorithm. Combined shape algorithm is embedded into genetic algorithm as a combined shape operator. Consequently a hybrid genetic algorithm for structural optimization with discrete variables is proposed. The constrained optimization problems were dealt with by adaptive annealing penalty factors and penalty function. The numerical results show that improved combined shape genetic algorithm for structural optimization with discrete variable problems has a faster convergence speed, which has advantages of local searching capability and globally searching capability of genetic algorithm. Improved combined shape genetic algorithm is an efficient optimal design method for engineering structure.

The Truss Structural Optimization Design Based on Improved Hybrid Genetic Algorithm

2010 ◽  
Vol 163-167 ◽  
pp. 2304-2308
Author(s):  
Feng Guo Jiang ◽  
Zhen Qing Wang
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Structural Optimization ◽  
Optimization Design ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Gradient Algorithm ◽  
General Search ◽  
Genetic Arithmetic ◽  
Iteration Analysis

Genetic arithmetic operators in genetic algorithm be improved , and a hybrid genetic algorithm of a gradient algorithm combining with the genetic algorithm be given against to the defects such as premature，slow on convergence rate，weak in the ability of local search ，all these appeared on the progress of genetic algorithm's iteration. Analysis result indicate that not only strong on the local search capacity of gradient algorithm be exhibited but also strong on the general search capacity of genetic algorithm be combined based on the hybrid genetic algorithm ,which make phenomenon of premature avoid, and the rate of convergence be improved greatly. Concrete calculated example indicated that the hybrid genetic algorithm is an effective structural optimization method.

Design of GA and Ontology based NLP Frameworks for Online Opinion Mining

2019 ◽  
Vol 13 (2) ◽  
pp. 159-165
Author(s):  
Manik Sharma ◽  
Gurvinder Singh ◽  
Rajinder Singh
Keyword(s):  
Genetic Algorithm ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Opinion Mining ◽  
Hybrid Genetic Algorithm ◽  
Online Reviews ◽  
Middle Tier ◽  
Complete Set ◽  
Mining Model

Background: For almost every domain, a tremendous degree of data is accessible in an online and offline mode. Billions of users are daily posting their views or opinions by using different online applications like WhatsApp, Facebook, Twitter, Blogs, Instagram etc. Objective: These reviews are constructive for the progress of the venture, civilization, state and even nation. However, this momentous amount of information is useful only if it is collectively and effectively mined. Methodology: Opinion mining is used to extract the thoughts, expression, emotions, critics, appraisal from the data posted by different persons. It is one of the prevailing research techniques that coalesce and employ the features from natural language processing. Here, an amalgamated approach has been employed to mine online reviews. Results: To improve the results of genetic algorithm based opining mining patent, here, a hybrid genetic algorithm and ontology based 3-tier natural language processing framework named GAO_NLP_OM has been designed. First tier is used for preprocessing and corrosion of the sentences. Middle tier is composed of genetic algorithm based searching module, ontology for English sentences, base words for the review, complete set of English words with item and their features. Genetic algorithm is used to expedite the polarity mining process. The last tier is liable for semantic, discourse and feature summarization. Furthermore, the use of ontology assists in progressing more accurate opinion mining model. Conclusion: GAO_NLP_OM is supposed to improve the performance of genetic algorithm based opinion mining patent. The amalgamation of genetic algorithm, ontology and natural language processing seems to produce fast and more precise results. The proposed framework is able to mine simple as well as compound sentences. However, affirmative preceded interrogative, hidden feature and mixed language sentences still be a challenge for the proposed framework.

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