scholarly journals Web Service Selection Using Modified Artificial Bee Colony Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 88673-88684 ◽  
Author(s):  
Manik Chandra ◽  
Rajdeep Niyogi
Keyword(s):  
Web Service ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Service Selection ◽  
Bee Colony ◽  
Web Service Selection

scholarly journals A Hybrid Artificial Bee Colony Algorithm for the Service Selection Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Changsheng Zhang ◽  
Bin Zhang
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Service Selection ◽  
Search Space ◽  
Selection Problem ◽  
Foraging Strategies ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Related Service ◽  
Bee Colony Optimization

To tackle the QoS-based service selection problem, a hybrid artificial bee colony algorithm calledh-ABC is proposed, which incorporates the ant colony optimization mechanism into the artificial bee colony optimization process. In this algorithm, a skyline query process is used to filter the candidates related to each service class, which can greatly shrink the search space in case of not losing good candidates, and a flexible self-adaptive varying construct graph is designed to model the search space based on a clustering process. Then, based on this construct graph, different foraging strategies are designed for different groups of bees in the swarm. Finally, this approach is evaluated experimentally using different standard real datasets and synthetically generated datasets and compared with some recently proposed related service selection algorithms. It reveals very encouraging results in terms of the quality of solutions.

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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