Evolutionary Multitasking for Semantic Web Service Composition
Chen Wang, Hui Ma, Gang Chen, Sven Hartmann

TL;DR
This paper introduces an evolutionary multi-tasking approach for semantic web service composition, effectively handling multiple similar requests with different preferences by jointly optimizing solutions and improving efficiency.
Contribution
It proposes a permutation-based multi-tasking evolutionary algorithm with a neighborhood structure to enhance web service composition efficiency and effectiveness.
Findings
Outperforms state-of-the-art single-task methods in efficiency.
Effectively handles multiple similar service requests with different preferences.
Neighborhood structure improves solution quality.
Abstract
Web services are basic functions of a software system to support the concept of service-oriented architecture. They are often composed together to provide added values, known as web service composition. Researchers often employ Evolutionary Computation techniques to efficiently construct composite services with near-optimized functional quality (i.e., Quality of Semantic Matchmaking) or non-functional quality (i.e., Quality of Service) or both due to the complexity of this problem. With a significant increase in service composition requests, many composition requests have similar input and output requirements but may vary due to different preferences from different user segments. This problem is often treated as a multi-objective service composition so as to cope with different preferences from different user segments simultaneously. Without taking a multi-objective approach that gives…
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Taxonomy
TopicsService-Oriented Architecture and Web Services · Advanced Software Engineering Methodologies
