NSGA-II, introduced by Kalyanmoy Deb et al., marked a significant milestone in evolutionary multi-objective optimization. It was designed to address the limitations of its predecessor, NSGA, by introducing a fast non-dominated sorting approach, a crowding distance assignment, and a selection mechanism that preserved diversity without requiring specification parameters. NSGA-II efficiently identifies a set of optimal solutions, known as the Pareto front, representing trade-offs among conflicting objectives. Despite its success, NSGA-II’s performance degrades when dealing with many-objective problems, where the number of objectives exceeds three or four. This degradation is primarily due to the crowding distance mechanism becoming less effective in higher-dimensional objective spaces.
Title: Innovations in Medicine Processing: Advancements Driving Pharmaceutical Manufacturing
Introduction: The pharmaceutical industry is continuously evolving, driven by advancements in technology, research, and manufacturing processes. Innovations in medicine processing play a pivotal role…