The recent advancements in optimization strategies for parallel computation and multi-objective optimization algorithms are significantly shaping the current landscape of the field. Researchers are increasingly focusing on developing novel bio-inspired metaheuristic optimization models, such as the Social Distancing Induced Coronavirus Optimization Algorithm (COVO), which leverages natural phenomena to tackle complex optimization problems. These models are designed to achieve faster convergence and global solutions, demonstrating promising results across various benchmark functions. Additionally, the field is witnessing a surge in methods for comparing multi-objective optimization algorithms, with a novel Pareto-optimal ranking method introduced to comprehensively assess algorithm performance using multiple metrics. This approach not only enhances the scalability and adaptability of evaluation techniques but also broadens their applicability across diverse scientific and engineering domains, including machine learning and data mining. Notably, these developments highlight a shift towards more integrated and multi-faceted evaluation frameworks in optimization research.