Multiobjective evolutionary algorithms based on target region preferences Li, L Wang, Y Trautmann, Heike Jing, N Emmerich, M Incorporating decision makers' preferences is of great significance in multiobjective optimization. Target region-based multiobjective evolutionary algorithms (TMOEAs), aiming at a well-distributed subset of Pareto optimal solutions within the user-provided region(s), are extensively investigated in this paper. An empirical comparison is performed among three TMOEA instantiations: T-NSGA-II, T-SMS-EMOA and T-R2-EMOA. Experimental results show that T-SMS-EMOA has the best overall performance regarding the hypervolume indicator within the target region, while T-NSGA-II is the fastest algorithm. We also compare TMOEAs with other state-of-the-art preference-based approaches, i.e., DF-SMS-EMOA, RVEA, AS-EMOA and R-NSGA-II to show the advantages of TMOEAs. A case study in the mission planning of earth observation satellite is carried out to verify the capabilities of TMOEAs in the real-world application. Experimental results indicate that preferences can improve the searching ability of MOEAs, and TMOEAs can successfully find nondominated solutions preferred by the decision maker. 2018 info:eu-repo/semantics/article doc-type:article text http://purl.org/coar/resource_type/c_6501 https://ris.uni-paderborn.de/record/46353 Li L, Wang Y, Trautmann H, Jing N, Emmerich M. Multiobjective evolutionary algorithms based on target region preferences. <i>Swarm and Evolutionary Computation</i>. 2018;40:196–215. doi:<a href="https://doi.org/10.1016/j.swevo.2018.02.006">10.1016/j.swevo.2018.02.006</a> eng info:eu-repo/semantics/altIdentifier/doi/10.1016/j.swevo.2018.02.006 info:eu-repo/semantics/closedAccess