Improvements to the Multi-Objective Optimization Algorithm NSGA-II

In the field of multi-objective optimization algorithms, the NSGA-II (Non-dominated Sorting Genetic Algorithm II) stands as a widely adopted methodology. While this algorithm typically requires fewer evolutionary generations compared to alternative approaches, it delivers remarkably superior final outcomes. To further enhance NSGA-II's performance, researchers have developed various improvements. For instance, implementations may incorporate modified selection strategies that optimize population diversity maintenance, or hybridize with differential evolution operators to strengthen global exploration capabilities. These enhancements significantly boost NSGA-II's convergence speed and solution quality. From a coding perspective, key improvements often involve restructuring the non-dominated sorting procedure using efficient data structures, implementing adaptive crossover/mutation operators, and adding elitism preservation mechanisms. Consequently, for solving multi-objective optimization problems, NSGA-II remains a highly valuable algorithm worthy of in-depth investigation and practical application.