Evolutionary Multitasking for Semantic Web Service Composition

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 rise to a solution selection challenge, we perceive multiple similar service composition requests as jointly forming an evolutionary multi-tasking problem in this work. We propose an effective permutation-based evolutionary multi-tasking approach that can simultaneously generate a set of solutions, with one for each service request. We also introduce a neighborhood structure over multiple tasks to allow newly evolved solutions to be evaluated on related tasks. Our proposed method can perform better at the cost of only a fraction of time, compared to one state-of-art single-tasking EC-based method. We also found that the use of the proper neighborhood structure can enhance the effectiveness of our approach.