Advancements in Multi-Objective Optimization: From NSGA-II to NSGA-III (updated and reviewed)

<p>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&rsquo;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.</p> <p><a href="https://medium.com/@evertongomede/advancements-in-multi-objective-optimization-from-nsga-ii-to-nsga-iii-updated-and-reviewed-86fad4ef0c57"><strong>Click Here</strong></a></p>