Grey wolf optimizer with learning algorithm for web of services

Abstract

Using a web service is like switching to a new operating system for the Internet.An essential component of the web service concept is a brokerage system that facilitates the publishing and retrieval of services from a searchable repository.In this study, we use a Distributed Constraint Optimization Problem (DCOP)-based agent-based approach to do this.Finding the best service based on device-specific quality-of-service criteria is the main objective of this research.We use a DFS (Depth First Search) tree-based parallel search technique.Using criteria including availability, affordability, safety, reliability, reaction time, and power consumption, DCOP allows several agents to work together to choose the best service to employ.As a result of using the DCOP approach, the devices' collected global constrain characteristics are validated.The fact that the DCOP algorithm generates a linearly proportional amount of messages is its primary strength.Here, messages of type UTIL and VALUE are exchanged.Agents may converse with their parents using UTIL messages and with their children using VALUE messages.The next step is to choose the best online service by using the agents.A multi-agent global constraint system, it expedites the acquisition of online services.