scholarly journals Performance Testing in a Multi Tenant Cloud Architecture using Genetic Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 2840-2846
Keyword(s):  
Genetic Algorithm ◽  
Resource Utilization ◽  
Performance Testing ◽  
Neighborhood Search ◽  
Base Population ◽  
Security Testing ◽  
User Interactions ◽  
Cloud Architecture ◽  
Testing Performance ◽  
Response Testing

Recent researches in cloud discusses about the application response testing, performance testing, security testing and many more, but still there is a lack of researches addressing issues like resource utilization and user interactions in cloud SaaS testing. The load on the cloud, SaaS instance keeps varying dynamically with respect to time, it is difficult to find the exact load at a particular interval of time. One does not know where to look for the solution and where to start, this made SaaS instances non deterministic in nature. In order to find a solution for such non deterministic problems, we make use of Genetic Algorithm which is considered as a good solution for non-deterministic problems.We determine the optimized resources that a cloud instance, would need to manage the dynamic load at all times. Toaddress the resource utilization of a group of users in MultiTenant Architecture (MTA), we adopt Genetic Algorithm which uses a popular technique, called neighborhood search and instance ranking policy. The basic concept of this paper is to explore the neighbors of an existing solution, that is considered as the solutions which can be obtained with a specific operation on the base population. In addition to that,this paper discusses about the ranking of all the available population and select the most highly ranked one. Instance ranking policies are aimed at minimizing the number of nodes in use or maximize the resources available to each node in an instance.

scholarly journals Two meta-heuristics for solving the multi-vehicle multi-covering tour problem with a constraint on the number of vertices

2020 ◽  
pp. 14-14
Author(s):  
Manel Kammoun ◽  
Houda Derbel ◽  
Bassem Jarboui
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Descent Method ◽  
Computational Experiments ◽  
Neighborhood Search ◽  
Variable Neighborhood Descent ◽  
Covering Tour Problem ◽  
Neighborhood Search Algorithm

In this work we deal with a generalized variant of the multi-vehicle covering tour problem (m-CTP). The m-CTP consists of minimizing the total routing cost and satisfying the entire demand of all customers, without the restriction of visiting them all, so that each customer not included in any route is covered. In the m-CTP, only a subset of customers is visited to fulfill the total demand, but a restriction is put on the length of each route and the number of vertices that it contains. This paper tackles a generalized variant of the m-CTP, called the multi-vehicle multi-covering Tour Problem (mm-CTP), where a vertex must be covered several times instead of once. We study a particular case of the mm-CTP considering only the restriction on the number of vertices in each route and relaxing the constraint on the length (mm-CTP-p). A hybrid metaheuristic is developet by combining Genetic Algorithm (GA), Variable Neighborhood Descent method (VND), and a General Variable Neighborhood Search algorithm (GVNS) to solve the problem. Computational experiments show that our approaches are competitive with the Evolutionary Local Search (ELS) and Genetic Algorithm (GA), the methods proposed in the literature.

scholarly journals Parameter Identification of Tractor-Semitrailer Model under Steering and Braking

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Yiming Li ◽  
Qin Shi ◽  
Duoyang Qiu
Keyword(s):  
Genetic Algorithm ◽  
Field Test ◽  
Test Data ◽  
Performance Testing ◽  
Roll Angle ◽  
Lookup Table ◽  
The Real ◽  
Yaw Rate ◽  
Roll Rate ◽  
Good Agreement

This paper describes a valuable linear yaw-roll tractor-semitrailer (TST) model with five-degree-of-freedom (DOFs) for control algorithm development when steering and braking. The key parameters, roll stiffness, axle cornering stiffness, and fifth-wheel stiffness, are identified by the genetic algorithm (GA) and multistage genetic algorithm (MGA) based on TruckSim outputs to increase the accuracy of the model. Thus, the key parameters of the simplified model can be modified according to the real-time vehicle states by online lookup table and interpolation. The TruckSim vehicle model is built referring to the real tractor (JAC-HFC4251P1K7E33ZTF6×2) and semitrailer (Luyue LHX9406) used in the field test later. The validation of the linear yaw-roll model of a tractor-semitrailer using field test data is presented in this paper. The field test in the performance testing ground is detailed, and the test data of roll angle, roll rate, and yaw rate are compared with the outputs of the model with maps of the key parameters. The results indicate that the error of the tractor’s roll angle and semitrailer’s roll angle between model data and test data is 1.13% and 1.24%, respectively. The roll rate and yaw rate of the tractor and semitrailer are also in good agreement.

scholarly journals Modeling the Municipal Waste Collection Using Genetic Algorithms

Processes ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 513
Author(s):  
Elisabete Alberdi ◽  
Leire Urrutia ◽  
Aitor Goti ◽  
Aitor Oyarbide-Zubillaga
Keyword(s):  
Genetic Algorithm ◽  
Variable Neighborhood Search ◽  
Real Data ◽  
Municipal Waste ◽  
Neighborhood Search ◽  
Initial Population ◽  
Waste Collection ◽  
Reduction Of Emissions ◽  
Apparent Reasons ◽  
Unsolved Issue

Calculating adequate vehicle routes for collecting municipal waste is still an unsolved issue, even though many solutions for this process can be found in the literature. A gap still exists between academics and practitioners in the field. One of the apparent reasons why this rift exists is that academic tools often are not easy to handle and maintain by actual users. In this work, the problem of municipal waste collection is modeled using a simple but efficient and especially easy to maintain solution. Real data have been used, and it has been solved using a Genetic Algorithm (GA). Computations have been done in two different ways: using a complete random initial population, and including a seed in this initial population. In order to guarantee that the solution is efficient, the performance of the genetic algorithm has been compared with another well-performing algorithm, the Variable Neighborhood Search (VNS). Three problems of different sizes have been solved and, in all cases, a significant improvement has been obtained. A total reduction of 40% of itineraries is attained with the subsequent reduction of emissions and costs.

Genetic Algorithms, Path Relinking, and the Flowshop Sequencing Problem

1998 ◽  
Vol 6 (1) ◽  
pp. 45-60 ◽  
Author(s):  
Colin R. Reeves ◽  
Takeshi Yamada
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Simulated Annealing Algorithm ◽  
Search Method ◽  
Neighborhood Search ◽  
Search Technique ◽  
Permutation Flowshop ◽  
Sequencing Problem ◽  
Annealing Algorithm ◽  
Tabu Search Method

In a previous paper, a simple genetic algorithm (GA) was developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem (PFSP). The performance of the algorithm was comparable to that of a naive neighborhood search technique and a proven simulated annealing algorithm. However, recent results have demonstrated the superiority of a tabu search method in solving the PFSP. In this paper, we reconsider the implementation of a GA for this problem and show that by taking into account the features of the landscape generated by the operators used, we are able to improve its performance significantly.

Low-Altitude Airship Remote Sensing Images Matching Based on Improved Hybrid Ant Colony Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 924-928
Author(s):  
Bang Long Pan ◽  
Wei Ning Yi ◽  
Xian Hua Wang
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithm ◽  
Data Processing ◽  
Image Matching ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Neighborhood Search ◽  
Registration Process ◽  
Low Altitude ◽  
Image Pairs

Low-altitude unmanned airship remote sensing is attractive to various applications. However, at present, since the airship is bulky, weak to resist wind, unstable to flight attitude, and can not be equipped with specialized remote sensing sensors, image data processing is confronted with new challenges when traditional data processing methods are used. In this paper, improved hybrid ant colony algorithm (HACA), a new image matching method, is proposed. Firstly we perform a pre-registration process that roughly aligns the image pairs by GPS/electronic compass geolocation. Once the pre-registration is completed, a fine-scale registration process is executed by applying a hybrid algorithm of genetic algorithm (GA) and ant colony algorithm (ACA) based on neighborhood search strategy that is detected by the linear quadtree Morton coding. The image pairs are then matched by using optimal solution obtained from the automatic updates of ant colony pheromone. By compared with traditional genetic algorithm and ant colony algorithm, the improved HACA results show that search calculation time is increased by the maximum 406℅, standard root mean square error of image matching by the best 235℅. The experiment result proves that improved HACA exactly provides an effective method for image matching. The local maxima of the function can be avoided efficiently and search speed of the global optimum is increased greatly.

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