Framework for Optimal Selection Using Meta-Heuristic Approach and AHP Algorithm

Many real-life decisions are focused on selecting the most preferable combination of available options, by satisfying different kinds of preferences and internal or external constraints and requirements. Focusing on the well-known analytical hierarchical process (AHP) method and its extension CS-AHP for capturing different kinds of preferences over two-layered structure (including conditionally defined preferences and preferences about dominant importance), we propose a two-layered framework for identifying stakeholders' decision criteria requirements and employ meta-heuristic algorithms (i.e., genetic algorithms) to optimally make a selection over available options. The proposed formal two-layered framework, called OptSelectionAHP, provides the means for optimal selection based on specified different kinds of preferences. The framework has simultaneously proven optimality applied in software engineering domain, for optimal configuration of business process families where stakeholders' preferences are defined over quality characteristics of available services (i.e., QoS attributes). Furthermore, this domain of application is characterized with uncertainty and variability in selection space, which is proven and does not significantly violate the optimality of the proposed framework.