Fuzzy teaching learning based optimization approach for solving the QoS-aware web service selection problem in uncertain environments

2021 ◽  
Author(s):  
Fateh Seghir ◽  
Ghizlane Khababa
Keyword(s):  
Web Service ◽  
Service Selection ◽  
Selection Problem ◽  
Optimization Approach ◽  
Uncertain Environments ◽  
Web Service Selection ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

scholarly journals Combining boolean consistent fuzzy logic and ahp illustrated on the web service selection problem

2013 ◽  
Vol 7 (sup1) ◽  
pp. 84-93 ◽  
Author(s):  
Ivana Dragović ◽  
Nina Turajlić ◽  
Dragan Radojević ◽  
Bratislav Petrović
Keyword(s):  
Fuzzy Logic ◽  
Web Service ◽  
Service Selection ◽  
Selection Problem ◽  
Web Service Selection ◽  
The Web

Applying gravitational search algorithm in the QoS-based Web service selection problem

2011 ◽  
Vol 12 (9) ◽  
pp. 730-742 ◽  
Author(s):  
Bahareh Zibanezhad ◽  
Kamran Zamanifar ◽  
Razieh Sadat Sadjady ◽  
Yousef Rastegari
Keyword(s):  
Web Service ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Service Selection ◽  
Selection Problem ◽  
Web Service Selection ◽  
Gravitational Search

QoS aware web service selection using orthogonal array learning on fruit fly optimization approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Manik Chandra ◽  
Rajdeep Niyogi
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Selection ◽  
Fruit Fly ◽  
Optimization Approach ◽  
Content Type ◽  
Fruit Fly Optimization ◽  
Bee Colony ◽  
Web Service Selection ◽  
Time Latency

Purpose This paper aims to solve the web service selection problem using an efficient meta-heuristic algorithm. The problem of selecting a set of web services from a large-scale service environment (web service repository) while maintaining Quality-of-Service (QoS), is referred to as web service selection (WSS). With the explosive growth of internet services, managing and selecting the proper services (or say web service) has become a pertinent research issue. Design/methodology/approach In this paper, to address WSS problem, the authors propose a new modified fruit fly optimization approach, called orthogonal array-based learning in fruit fly optimizer (OL-FOA). In OL-FOA, they adopt a chaotic map to initialize the population; they add the adaptive DE/best/2mutation operator to improve the exploration capability of the fruit fly approach; and finally, to improve the efficiency of the search process (by reducing the search space), the authors use the orthogonal learning mechanism. Findings To test the efficiency of the proposed approach, a test suite of 2500 web services is chosen from the public repository. To establish the competitiveness of the proposed approach, it compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC). The empirical results show that the proposed approach outperforms its counterparts in terms of response time, latency, availability and reliability. Originality/value In this paper, the authors have developed a population-based novel approach (OL-FOA) for the QoS aware web services selection (WSS). To justify the results, the authors compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC) over the four QoS parameter response time, latency, availability and reliability. The authors found that the approach outperforms overall competitive approaches. To satisfy all objective simultaneously, the authors would like to extend this approach in the frame of multi-objective WSS optimization problem. Further, this is declared that this paper is not submitted to any other journal or under review.

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