scholarly journals An Improved Genetic Algorithm for Web Services Selection

2007 ◽  
pp. 284-295 ◽  
Author(s):  
Sen Su ◽  
Chengwen Zhang ◽  
Junliang Chen
Keyword(s):  
Genetic Algorithm ◽  
Web Services ◽  
Improved Genetic Algorithm

An Improved Genetic Algorithm of Web Services Composition with QOS

2012 ◽  
Vol 532-533 ◽  
pp. 1836-1840 ◽  
Author(s):  
Yan Yan Zhang ◽  
Hai Ling Xiong ◽  
Yong Chun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Web Services ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Experimental Result ◽  
Web Services Composition ◽  
Improved Genetic Algorithm ◽  
Search Range ◽  
Services Composition ◽  
Fitness Value

A web service composition method based on the adaptive genetic operator was proposed to deal with the issues of the lack of adaptability and the easy-premature phenomena in web services composition genetic algorithm. Adaptive crossover and mutation operator were designed according to the individual adaptability and evolution stage for enlarging local search range and increasing convergent speed. Moreover, use for reference the idea of taboo table in taboo search algorithm, we can inhibit the algorithm from converging to false optimal solution untimely; meanwhile, an evolution strategy was adopted to prevent the loss of composite service with high fitness value. The experimental result shows that better composite services can be gotten through the improved algorithm; moreover the convergence speed has also been improved.

QoS-based Web Service Composition Applying an Improved Genetic Algorithm (IGA) Method

2016 ◽  
Vol 12 (3) ◽  
pp. 60-77 ◽  
Author(s):  
Pooya Shahrokh ◽  
Faramarz Safi-Esfahani
Keyword(s):  
Genetic Algorithm ◽  
Web Services ◽  
Web Service ◽  
Service Composition ◽  
Heuristic Method ◽  
Web Service Composition ◽  
Improved Genetic Algorithm ◽  
Functional Factors ◽  
Research Findings ◽  
The Difference

In recent years, it has been made possible to compose exiting services when a user's request cannot be satisfied by a single web service. Web service composition is faced with several challenges among which is the rapid growth in the number of available web services leading to increased number of web services offering the same functionalities. The difference between similar services is Quality of Service (QoS) consisting of various non-functional factors such as execution time, availability, security, etc. As a result, multiple choices are possible in making a composition plan. Among numerous plans, selecting a composition plan that fulfills customer's requirements has become an important and time-consuming problem. In this paper, the researchers propose a semi-heuristic genetic algorithm that is a combination of both a heuristic method and the genetic algorithm. This heuristic method changes chromosomes based on unsatisfied constraints. Research findings show that the proposed method can be applied to find a composition plan that satisfies user's requirements more efficiently than other methods.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals IGAEM: Improved Genetic Algorithm based Entity Matching

2021 ◽  
Vol 1743 ◽  
pp. 012001
Author(s):  
Y. Aassem ◽  
I. Hafidi ◽  
N. Aboutabit
Keyword(s):  
Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
Entity Matching
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