Evolutionary Multitasking for Semantic Web Service Composition
  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.
