Search-Based Adaptation Planning Framework for Self-Adaptive Systems

Future-generation Self-Adaptive Systems (SASs) are required to adapt to the multiple, interrelated, and evolving changes. Current adaptation planning methods, which consider only one or two changes at a time and assume that changes are independent and the prioritization of them is static, need to be improved. Arguing that the adaptation planning is a search problem, this thesis highlights the feasibility and potential benefits of adopting Search-Based Optimization as an innovative planning method. A search-based adaptation planning framework is proposed to deal with these changes and make the best decisions for future-generation SASs.