NSGA-II (Non-dominated Sorting Genetic Algorithm II) if by far the most well known and most used multi-objective optimization metaheuristic. It was the first algorithm implemented in jMetal and currently the framework provides a number of versions.
- ssNSGAII (steady-state NSGA-II). NSGA-II is a generational genetic algorithm and ssNSGA-II is the steady-state version of it.
- pNSGAII (parallel NSGA-II). This version can take advantage of the multicores of current processors to perform the function evaluations of different individuals in parallel.
- NSGAIIr (random NSGA-II). NSGA-IIr is basically NSGA-II buth three variation operators (SBX crossover, polynomial mutation and differential evolution) are selected randomly to create new individuals.
- NSGAIIa (adaptive NSGA-II). This algorithm works as NSGA-IIr, but the operators are selected adaptively.
- A.J. Nebro, J.J. Durillo, On the Effect of Applying a Steady-State Selection Scheme in the Multi-Objective Genetic Algorithm NSGA-II. In R. Chion (Ed.): Nature-Inspired Algorithms for Optimization, pp. 435 - 456, Springer 2009. DOI
- Antonio J. Nebro, Juan J. Durillo, Mirialys Machín, Carlos A. Coello Coello, Bernabé Dorronsoro, A Study of the Combination of Variation Operators in the NSGA-II Algorithm. Proceedings of the 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013. Lecture Notes in Computer Science Volume 8109, 2013, pp 269-278. DOI