Numerical Optimization Algorithm for Unsteady Flows of Rotor based on Web Service

2019 ◽  
pp. -1--1
Author(s):  
Jilin Zhang ◽  
Xuechao Liu ◽  
Jian Wan ◽  
Yongjian Ren ◽  
Binglin Xu ◽  
...  
Keyword(s):  
Web Service ◽  
Optimization Algorithm ◽  
Numerical Optimization ◽  
Unsteady Flows

QoS–Aware Web Service Composition using Weight Improved Particle Swarm Optimization

2021 ◽  
Vol 9 (2) ◽  
pp. 65-70
Author(s):  
Laishram Jenny Chanu ◽  
◽  
Arnab Paul ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Web Services ◽  
Web Service ◽  
Optimization Algorithm ◽  
Service Composition ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Web Service Composition ◽  
Swarm Optimization ◽  
Complex Query

Lots of Web Services are available which differ in their QoS values but can perform a similar task. Discovery mechanism selects the best Web Service according to their QoS values and functional attributes. Cases arise, where the discovery mechanism fails, as a user’s complex query cannot be satisfied by a single Web Service. This can be solved by Web Service composition where multiple Web Services are combined to give a composite Web Service which meet user’s complex query. Our work is mainly focused on composition of Web Services that efficiently meets the user’s query. Different algorithms have been discussed and used by different researchers in this field. One of the most blooming topics is the use of evolutionary algorithms in optimization problems. In our work, we have chosen Particle Swarm Optimization Algorithm approach to discover the best efficient composition. Then, Weight Improved Particle Swarm Optimization Algorithm is used to improve the results which were found to be quite satisfying and efficient.

scholarly journals Insights into Exploration and Exploitation Power of Optimization Algorithm Using DSCTool

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1474
Author(s):  
Peter Korošec ◽  
Tome Eftimov
Keyword(s):  
Statistical Analysis ◽  
Web Service ◽  
Optimization Algorithm ◽  
Optimization Algorithms ◽  
Search Space ◽  
Exploration And Exploitation ◽  
Learning Tool ◽  
Additional Information ◽  
E Learning ◽  
Single Objective

When making statistical analysis of single-objective optimization algorithms’ performance, researchers usually estimate it according to the obtained optimization results in the form of minimal/maximal values. Though this is a good indicator about the performance of the algorithm, it does not provide any information about the reasons why it happens. One possibility to get additional information about the performance of the algorithms is to study their exploration and exploitation abilities. In this paper, we present an easy-to-use step by step pipeline that can be used for performing exploration and exploitation analysis of single-objective optimization algorithms. The pipeline is based on a web-service-based e-Learning tool called DSCTool, which can be used for making statistical analysis not only with regard to the obtained solution values but also with regard to the distribution of the solutions in the search space. Its usage does not require any special statistic knowledge from the user. The gained knowledge from such analysis can be used to better understand algorithm’s performance when compared to other algorithms or while performing hyperparameter tuning.

Process-Monitor’s Multimodal Optimization with Dynamic Cloning Based Immune Network Algorithm

2013 ◽  
Vol 662 ◽  
pp. 781-787 ◽  
Author(s):  
Yu Guang Zhu ◽  
Xu Hua Shi ◽  
Xiong Yang ◽  
Li Xiang Shen
Keyword(s):  
Process Monitoring ◽  
Optimization Algorithm ◽  
Numerical Optimization ◽  
Experimental Results ◽  
Immune Network ◽  
Search Performance ◽  
Multimodal Optimization ◽  
Global Numerical Optimization ◽  
Network Mechanism ◽  
Process Monitor

A novel process-monitor multimodal optimization algorithm, called Pmdcopt-aiNet is given. It is based on biological immune network mechanism for process-monitor global-numerical optimization. The Pmdcopt-aiNet models can clone the process-monitor operation using dynamic cloning operation which is adopted from biological immune network mechanism. The experiments based on the multimodal benchmarks were carried out to compare the performance of Pmdcopt-aiNet with that of other existing algorithms. The experimental results in process monitoring show that the new algorithm is capable of improving search performance significantly in successful rate and convergence speed when compared with the already existing method.

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