{"doi":"10.1109/4235.996017","title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","abstract":"Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism approach; and (3) the need to specify a sharing parameter. In this paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN/sup 2/) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best N solutions (with respect to fitness and spread). Simulation results on difficult test problems show that NSGA-II is able, for most problems, to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the Pareto-archived evolution strategy and the strength-Pareto evolutionary algorithm - two other elitist MOEAs that pay special attention to creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint nonlinear problem, are compared with another constrained multi-objective optimizer, and the much better performance of NSGA-II is observed.","journal":"IEEE Transactions on Evolutionary Computation","year":2002,"id":12008,"datarank":19.337471181609217,"base_score":10.75698756830457,"endowment":10.75698756830457,"self_citation_contribution":1.6135481352456857,"citation_network_contribution":17.72392304636353,"self_endowment_contribution":1.6135481352456857,"citer_contribution":17.72392304636353,"corpus_percentile":99.2,"corpus_rank":75,"citation_count":46956,"citer_count":180,"citers_with_citation_signal":180,"citers_with_endowment":180,"datacite_reuse_total":0,"is_dataset":false,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2002-04-01","authors":[{"id":95911,"name":"A. Pratap","orcid":null,"position":1,"is_corresponding":false},{"id":20658,"name":"S. Agarwal","orcid":"0000-0002-2350-4610","position":2,"is_corresponding":false},{"id":95912,"name":"T. Meyarivan","orcid":null,"position":3,"is_corresponding":false},{"id":58929,"name":"Kalyanmoy Deb","orcid":"0000-0001-7402-9939","position":4,"is_corresponding":false},{"id":95913,"name":"Amrit Pratap","orcid":null,"position":5,"is_corresponding":false},{"id":95914,"name":"Sakshi Agarwal","orcid":null,"position":6,"is_corresponding":false}],"reference_count":26,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}