As the number of cloud services from multi-cloud providers grows, the selection of proper cloud services to optimize multiple, potentially conflicting, objectives simultaneously is an issue. In the second paper of this Special Issue, titled \"Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization\" by [PERSON] et al. [6], the authors analyzed cloud computing with effective resource utilization and cost optimization. To overcome the high resource cost, the authors proposed a new model called Classification and Merging Techniques for Reducing Brokerage Cost (CMRBC) designed for effective resource utilization and cost optimization in the cloud. CMRBC appears to have two benefits. Firstly, it is a cost-effective solution for service providers and customers. Secondly, for every job, Virtual Machine (VM) creations are avoided to reduce brokerage. The allocation, creation, or selection of resources of VM is carried out by the broker. The main objectives considered are to maximize resource utilization and minimize brokerage in cloud computing by using MOO. Likewise, CMRBC implements efficient resource allocation to reduce the usage cost of resources, as demonstrated in the experimental campaign carried out by the authors. Credit scoring is an important tool for banks and financial institutions to measure credit risk. It consists of a statistical analysis performed by lenders and financial institutions to determine the creditworthiness of a person or a company. Typically, these problems are tackled with Linear Discriminant Analysis (LDA), which, according to the score of each credit applicant, categorizes these applicants by a cutoff and is a comprehensible and robust method in the credit scoring domain. However, enhancing credit scoring models based on LDA is an important research target, as observed in, e.g., [PERSON] et al. [7], placing particular emphasis on improving the sensitivity of the classifier by using multi-objective optimization. The third paper of the Special Issue titled \"Comparison of Profit-Based Multi-Objective Approaches for Feature Selection in Credit Scoring\" by [PERSON] et al. [8] concentrates its focus exactly on the latter topic and in particular on feature selection, which is crucial to credit-scoring process, allowing for the removal of irrelevant variables with low predictive power. Indeed, conventional credit-scoring techniques treat this as a separate process wherein features are selected based on improving a single statistical measure, such as accuracy; however, recent research has focused on meaningful business parameters such as profit. More than one factor may be important to the selection process, making multi-objective optimization methods relevant in attaining such a goal. However, the comparative performance of multi-objective methods has been known to vary depending on the test problem and specific implementation. This research employed a recent hybrid non-dominated sorting binary Grasshopper Optimization Algorithm and compared its performance on multi-objective feature selection for credit scoring to that of two popular benchmark algorithms in this space. Further comparison is made to determine the impact of changing the profit-maximizing base classifiers on algorithm performance. The experimental campaign carried out by the authors demonstrated that the neural network classifier improved the profit-based measure and minimized the mean number of features in the population the most. Additionally, the authors' algorithm gave relatively smaller hypervolumes and increased computational time across all base classifiers, while giving the highest mean objective values for the solutions. The last application considered in this Special Issue is related to the COVID-19 pandemic outbreak. Pandemic influenza has been an important public health concern, with several historical outbreaks.