Integration of Cloud-Fog Based Platform for Load Balancing Using Hybrid Genetic Algorithm Using Bin Packing Technique

2018 ◽  
pp. 279-292 ◽  
Author(s):  
Muhammad Zubair ◽  
Nadeem Javaid ◽  
Muhammad Ismail ◽  
Muhammad Zakria ◽  
Muhammad Asad Zaheer ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Load Balancing ◽  
Bin Packing ◽  
Hybrid Genetic Algorithm

scholarly journals Load Planning of Transport Aircraft Based on Hybrid Genetic Algorithm

2018 ◽  
Vol 179 ◽  
pp. 01007
Author(s):  
Yang Chenguang ◽  
Liu Hu ◽  
Gao Yuan
Keyword(s):  
Genetic Algorithm ◽  
Bin Packing ◽  
Hybrid Genetic Algorithm ◽  
Transport Costs ◽  
Gravity Center ◽  
Transport Aircraft ◽  
Load Planning ◽  
Volume Weight ◽  
Loading Problem

Loading of transport aircraft attracts much attention as the airlift is developing rapidly. It refers to the process that various cargoes are loaded, in an appropriate manner, into kinds of transport aircrafts with constraints of volume, weight and gravity center. Based on two-dimensional bin packing with genetic algorithm (GA), a new hybrid algorithm is proposed to solve the multi-constraint loading problem of transport aircraft for seeking the minimum of fuel consumption. Heuristic algorithm is applied to optimize single-aircraft loading in GA decoding, and the procedure of hybrid GA is summarized for the multi-aircraft loading issues. In the case study, eight kinds of cargos are distributed in three different aircrafts. The optimal result indicates that this algorithm can rapidly generate the best plan for the loading problem regarding lower transport costs.

A Hybrid Genetic Algorithm for Hybrid Flow Shop Scheduling with Load Balancing

2008 ◽  
Vol 392-394 ◽  
pp. 250-255
Author(s):  
Yong Zhan ◽  
Chang Hua Qiu ◽  
Kai Xue
Keyword(s):  
Genetic Algorithm ◽  
Load Balancing ◽  
Parallel Machines ◽  
Flow Shop ◽  
Hybrid Genetic Algorithm ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Hybrid Flow Shop ◽  
Initial Population ◽  
Hybrid Flow Shop Scheduling

This paper considers the practical manufacturing environment of the hybrid flow shop (HFS) with non-identical machines in parallel. In order to significantly enhance the performance level of manufacturing, maintaining load balancing among parallel machines is very important. The aim of this paper is to minimize makespan with load balancing in a non-identical parallel machine environment by using hybrid genetic algorithm (HGA). In the HGA, the neighborhood search-based method is used together with genetic algorithm as local optimization method to balance the exploration and exploitation abilities. The representation of chromosome used in this paper is composed of two layers: allocation layer and sequencing layer, which can be encode and decoded easily. In generating initial population, a special constraint of load balancing between parallel machines is used to reduce the number of individuals. And particular crossover operation is used, which generates multiple offspring at a time, so that the efficiency of the algorithm can be well improved. At last, the proposed algorithm is tested on a benchmark, and numerical example shows good result.

The Improvement of a Hybrid Genetic Algorithm for Three Dimensional Bin-Packing Problems

2012 ◽  
Vol 200 ◽  
pp. 470-473
Author(s):  
Zhen Zhai ◽  
Li Chen ◽  
Xiao Min Han
Keyword(s):  
Genetic Algorithm ◽  
Bin Packing ◽  
Hybrid Genetic Algorithm ◽  
Three Dimensional ◽  
Packing Problem ◽  
Flow Process ◽  
Packing Problems ◽  
Loading Problem ◽  
The Cost ◽  
Process Diagrams

The multi-constrained bi-objective bin packing problem has many extensive applications. In the loading section of logistics it has mainly been transported by truck. The cost of transportation is not only determined by the bin space utilization, but also by the number of vehicles in transporta¬tion utilization. The type of items and bins is introduced in the mathematical model, as well as the volume of the items. In this paper, the hybrid genetic algorithm which tabu and simulated annealed rules are added for complex container-loading problem is studied. The effective coding and decod-ing method together with flow process diagrams are given.

Evaluation of Accidental Death Records Using Hybrid Genetic Algorithm

2020 ◽  
Author(s):  
Nikhil Sharma ◽  
Ila Kaushik ◽  
Rajat Rathi ◽  
Santosh Kumar
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Accidental Death

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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