[{"volume":40,"user_id":"15504","doi":"10.1016/j.swevo.2018.02.006","_id":"46353","language":[{"iso":"eng"}],"page":"196–215","intvolume":"        40","date_updated":"2023-10-16T13:34:21Z","author":[{"last_name":"Li","first_name":"L","full_name":"Li, L"},{"last_name":"Wang","first_name":"Y","full_name":"Wang, Y"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"},{"last_name":"Jing","first_name":"N","full_name":"Jing, N"},{"last_name":"Emmerich","first_name":"M","full_name":"Emmerich, M"}],"year":"2018","title":"Multiobjective evolutionary algorithms based on target region preferences","status":"public","department":[{"_id":"34"},{"_id":"819"}],"type":"journal_article","date_created":"2023-08-04T07:56:57Z","abstract":[{"lang":"eng","text":"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."}],"citation":{"mla":"Li, L., et al. “Multiobjective Evolutionary Algorithms Based on Target Region Preferences.” <i>Swarm and Evolutionary Computation</i>, vol. 40, 2018, pp. 196–215, doi:<a href=\"https://doi.org/10.1016/j.swevo.2018.02.006\">10.1016/j.swevo.2018.02.006</a>.","ama":"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>","bibtex":"@article{Li_Wang_Trautmann_Jing_Emmerich_2018, title={Multiobjective evolutionary algorithms based on target region preferences}, volume={40}, DOI={<a href=\"https://doi.org/10.1016/j.swevo.2018.02.006\">10.1016/j.swevo.2018.02.006</a>}, journal={Swarm and Evolutionary Computation}, author={Li, L and Wang, Y and Trautmann, Heike and Jing, N and Emmerich, M}, year={2018}, pages={196–215} }","apa":"Li, L., Wang, Y., Trautmann, H., Jing, N., &#38; Emmerich, M. (2018). Multiobjective evolutionary algorithms based on target region preferences. <i>Swarm and Evolutionary Computation</i>, <i>40</i>, 196–215. <a href=\"https://doi.org/10.1016/j.swevo.2018.02.006\">https://doi.org/10.1016/j.swevo.2018.02.006</a>","ieee":"L. Li, Y. Wang, H. Trautmann, N. Jing, and M. Emmerich, “Multiobjective evolutionary algorithms based on target region preferences,” <i>Swarm and Evolutionary Computation</i>, vol. 40, pp. 196–215, 2018, doi: <a href=\"https://doi.org/10.1016/j.swevo.2018.02.006\">10.1016/j.swevo.2018.02.006</a>.","short":"L. Li, Y. Wang, H. Trautmann, N. Jing, M. Emmerich, Swarm and Evolutionary Computation 40 (2018) 196–215.","chicago":"Li, L, Y Wang, Heike Trautmann, N Jing, and M Emmerich. “Multiobjective Evolutionary Algorithms Based on Target Region Preferences.” <i>Swarm and Evolutionary Computation</i> 40 (2018): 196–215. <a href=\"https://doi.org/10.1016/j.swevo.2018.02.006\">https://doi.org/10.1016/j.swevo.2018.02.006</a>."},"publication":"Swarm and Evolutionary Computation"}]