Refactoring is widely adopted nowadays in the industry due to the growing complexity of software systems. Many of the existing refactoring tools and research are based on search-based techniques to find relevant recommendations to improve the quality. While these techniques show promising results on open-source and industry projects, they lack explainability of the recommended changes which can impact their trustworthiness when adopted in practice by developers. Furthermore, most of the adopted search-based techniques are based on random population generation and random change operators (crossover and mutation) despite that refactoring is a discrete problem. It is critical to keep good patterns in the refactoring solutions when applying change operators; something cannot be done randomly. The initialization of the population is one of the keys to generating relevant solutions in reasonable execution time. In this paper, we propose an enhanced knowledge-informed multi-objective search algorithm, called X-SBR, to provide explanations for refactoring solutions and generate personalized and relevant ones. We generate, first, association rules using the Apriori algorithm to find relationships between applied refactorings in previous commits, their locations, and rationale (quality improvements). Then, we used these rules to 1) initialize the population, 2) improve the change operators and seeding mechanisms of the multi-objective search to keep and exchange good patterns in the refactoring solutions, and 3) explain the benefits of applying the generated refactorings. The results show that X-SBR has three main advantages comparing to the state-of-the-art based on both quantitative and qualitative validation on large open-source and industrial projects---faster execution time, a better quality of refactoring solutions, and increased trustworthiness in the suggested refactorings by the participants based on the generated explanations.