Cloud Resource Combinatorial Double Auction Algorithm Based on Genetic Algorithm and Simulated Annealing

2017 ◽  
pp. 435-445
Author(s):  
Bing Hu ◽  
Lin Yao ◽  
Yong Chen ◽  
Zinxin Sun
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Double Auction ◽  
Auction Algorithm ◽  
Cloud Resource

scholarly journals Scientific Applications in the Cloud: Resource Optimisation based on Metaheuristics

2020 ◽  
Vol 21 (4) ◽  
pp. 649-660
Author(s):  
Anas Mokhtari ◽  
Mostafa Azizi ◽  
Mohammed Gabli
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Cost Reduction ◽  
Complex Task ◽  
Scientific Applications ◽  
Cloud Data ◽  
Cloud Resource ◽  
Data Centres ◽  
Different Levels

The advent of emerging technologies such as 5G and Internet of Things (IoT) will generate a colossal amount ofdata that should be processed by the cloud computing. Thereby, cloud resources optimisation represents significant benefits in different levels: cost reduction for the user, saving energy consumed by cloud data centres, etc. Cloud resource optimisation is a very complex task due to its NP-hard characteristic. In this case, use of metaheuristic approaches is more rational. But the quality of metaheuristic solutions changes by changing the problem. In this paper we have dealt with the problem of determining the configuration of resources in order to minimise the payment cost and the duration of the scientific applications execution. For that, we proposed a mathematical model and three metaheuristic approaches, namely the Genetic Algorithm (GA), hybridisation of the Genetic Algorithm with Local Search (GA-LS) and the Simulated Annealing (SA). The comparison between them showed that the simulated annealing finds more optimal solutions than those proposed by the genetic algorithm and the GA-LS hybridisation.

Parallel recombinative simulated annealing: A genetic algorithm

1995 ◽  
Vol 21 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Samir W. Mahfoud ◽  
David E. Goldberg
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing

Memetic Algorithm Approach to Two-Stage Hybrid Flow Shop Scheduling Problem with Identical Parallel Machines

2013 ◽  
Vol 651 ◽  
pp. 548-552
Author(s):  
Parinya Kaweegitbundit
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Parallel Machines ◽  
Flow Shop ◽  
Memetic Algorithm ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Two Stage ◽  
Hybrid Flow Shop Scheduling

This paper considers two stage hybrid flow shop (HFS) with identical parallel machine. The objectives is to determine makespan have been minimized. This paper presented memetic algorithm procedure to solve two stage HFS problems. To evaluated performance of propose method, the results have been compared with two meta-heuristic, genetic algorithm, simulated annealing. The experimental results show that propose method is more effective and efficient than genetic algorithm and simulated annealing to solve two stage HFS scheduling problems.

Short Term Electric Load Forecasting with Back Propagation Neural Network and Simulated Annealing Genetic Algorithm

2015 ◽  
Vol 785 ◽  
pp. 14-18 ◽  
Author(s):  
Badar ul Islam ◽  
Zuhairi Baharudin ◽  
Perumal Nallagownden
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Forecast Accuracy ◽  
Back Propagation Neural Network ◽  
Search Performance ◽  
Training Algorithm ◽  
Short Term

Although, Back Propagation Neural Network are frequently implemented to forecast short-term electricity load, however, this training algorithm is criticized for its slow and improper convergence and poor generalization. There is a great need to explore the techniques that can overcome the above mentioned limitations to improve the forecast accuracy. In this paper, an improved BP neural network training algorithm is proposed that hybridizes simulated annealing and genetic algorithm (SA-GA). This hybrid approach leads to the integration of powerful local search capability of simulated annealing and near accurate global search performance of genetic algorithm. The proposed technique has shown better results in terms of load forecast accuracy and faster convergence. ISO New England data for the period of five years is employed to develop a case study that validates the efficacy of the proposed technique.

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