Integrated probability multi-search and solution acceptance rule-based artificial bee colony optimization scheme for web service composition

Web service composition is considered as the hottest and potential research area in the domain of Service Oriented Architecture since the users focus on Quality of Service (QoS) and transaction properties included in the integration of services. Moreover, the potential quality of modularity and reusability features of web services has wide open the feasible options of integrating diversified function oriented services together with the better optimization capability. Hence, a meta-heuristic approach-based web service composition scheme is essential for facilitating superior and comprehensive quality during the process of integrating services. In this paper, An Integrated Probability Multi-search and Solution Acceptance Rule-based Artificial Bee Colony Optimization Scheme (IPM-SAR-ABCOS) is proposed for optimizing the process of service compositions derived using transaction and QoS characteristics of services. This proposed IPM-SAR-ABCOS is efficient in determining the optimal path that exists between the source and sink vertex of the workflow inspired directed acyclic graph that aids in predominant service composition. The proposed IPM-SAR-ABCOS uses the rules of acceptance and multi-search probabilistic parameter for addressing the process of global optimization in service composition. The experimental analysis of the proposed IPM-SAR-ABCOS inferred that its response time, accuracy and recall value is enhanced by 24%, 22% and 19% excellent to the ABC-based meta-heuristic service composition techniques considered for analysis.

Automated summary

Web service composition is a crucial research area in Service Oriented Architecture, focusing on Quality of Service and transaction properties. A meta-heuristic approach is essential for integrating diverse function-oriented services with optimization capability, leading to superior quality. The proposed IPM-SAR-ABCOS optimizes service compositions through transaction and QoS characteristics, enhancing response time, accuracy, and recall values compared to other meta-heuristic techniques.