scholarly journals An improved hybrid genetic algorithm for holes machining path optimization using helical milling operation

2021 ◽  
Vol 1798 (1) ◽  
pp. 012035
Author(s):  
Z Q Li ◽  
X Liu ◽  
L S Duan ◽  
L Liu
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Path Optimization ◽  
Helical Milling ◽  
Milling Operation

RDF Query Path Optimization Using Hybrid Genetic Algorithms

2022 ◽  
Vol 12 (1) ◽  
pp. 1-16
Author(s):  
Qazi Mudassar Ilyas ◽  
Muneer Ahmad ◽  
Sonia Rauf ◽  
Danish Irfan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Execution Time ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Solution Space ◽  
Path Optimization ◽  
Initial Population ◽  
Description Framework ◽  
The Cost

Resource Description Framework (RDF) inherently supports data mergers from various resources into a single federated graph that can become very large even for an application of modest size. This results in severe performance degradation in the execution of RDF queries. As every RDF query essentially traverses a graph to find the output of the Query, an efficient path traversal reduces the execution time of RDF queries. Hence, query path optimization is required to reduce the execution time as well as the cost of a query. Query path optimization is an NP-hard problem that cannot be solved in polynomial time. Genetic algorithms have proven to be very useful in optimization problems. We propose a hybrid genetic algorithm for query path optimization. The proposed algorithm selects an initial population using iterative improvement thus reducing the initial solution space for the genetic algorithm. The proposed algorithm makes significant improvements in the overall performance. We show that the overall number of joins for complex queries is reduced considerably, resulting in reduced cost.

ACO-Based Holes Machining Path Optimization Using Helical Milling Operation

2013 ◽  
Vol 834-836 ◽  
pp. 1386-1390
Author(s):  
Zhong Qun Li ◽  
Xin Wang ◽  
Ya Feng Dong
Keyword(s):  
Path Optimization ◽  
Helical Milling ◽  
Aircraft Industry ◽  
Metal Compounds ◽  
Milling Operation ◽  
High Efficient ◽  
Optimization Simulation ◽  
Hole Making ◽  
New Type ◽  
Simulation Results

As a new-type, high efficient hole-making technology, helical milling is widely used in making holes on composites and composite-metal compounds materials in the aircraft industry. In this paper, the problem of machining path optimization using helical milling is converted to a TSP (Traveling Salesman Problem), a mathematical model is established and solved using ACO (Ant Colony Optimization). Simulation results show that with the application of ACO, the traveling efficiency of helical milling has been increased by 41.1%.

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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