{"doi":"10.1063/1.5090019","date_created":"2023-08-04T07:48:15Z","_id":"46342","status":"public","author":[{"last_name":"Grimme","full_name":"Grimme, Christian","first_name":"Christian"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"},{"first_name":"Michael T M","full_name":"Emmerich, Michael T M","last_name":"Emmerich"},{"first_name":"Mike","last_name":"Preuss","full_name":"Preuss, Mike"},{"first_name":"André H","last_name":"Deutz","full_name":"Deutz, André H"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","id":"100740","first_name":"Heike","orcid":"0000-0002-9788-8282"}],"year":"2019","user_id":"15504","publisher":"AIP Publishing","type":"conference","citation":{"ama":"Grimme C, Kerschke P, Emmerich MTM, Preuss M, Deutz AH, Trautmann H. Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization. In: <i>AIP Conference Proceedings</i>. AIP Publishing; 2019:020052-1-020052-020054. doi:<a href=\"https://doi.org/10.1063/1.5090019\">10.1063/1.5090019</a>","bibtex":"@inproceedings{Grimme_Kerschke_Emmerich_Preuss_Deutz_Trautmann_2019, place={Leiden, The Netherlands}, title={Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization}, DOI={<a href=\"https://doi.org/10.1063/1.5090019\">10.1063/1.5090019</a>}, booktitle={AIP Conference Proceedings}, publisher={AIP Publishing}, author={Grimme, Christian and Kerschke, Pascal and Emmerich, Michael T M and Preuss, Mike and Deutz, André H and Trautmann, Heike}, year={2019}, pages={020052-1-020052–4} }","apa":"Grimme, C., Kerschke, P., Emmerich, M. T. M., Preuss, M., Deutz, A. H., &#38; Trautmann, H. (2019). Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization. <i>AIP Conference Proceedings</i>, 020052-1-020052–020054. <a href=\"https://doi.org/10.1063/1.5090019\">https://doi.org/10.1063/1.5090019</a>","ieee":"C. Grimme, P. Kerschke, M. T. M. Emmerich, M. Preuss, A. H. Deutz, and H. Trautmann, “Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization,” in <i>AIP Conference Proceedings</i>, 2019, pp. 020052-1-020052–4, doi: <a href=\"https://doi.org/10.1063/1.5090019\">10.1063/1.5090019</a>.","short":"C. Grimme, P. Kerschke, M.T.M. Emmerich, M. Preuss, A.H. Deutz, H. Trautmann, in: AIP Conference Proceedings, AIP Publishing, Leiden, The Netherlands, 2019, pp. 020052-1-020052–4.","mla":"Grimme, Christian, et al. “Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization.” <i>AIP Conference Proceedings</i>, AIP Publishing, 2019, pp. 020052-1-020052–54, doi:<a href=\"https://doi.org/10.1063/1.5090019\">10.1063/1.5090019</a>.","chicago":"Grimme, Christian, Pascal Kerschke, Michael T M Emmerich, Mike Preuss, André H Deutz, and Heike Trautmann. “Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization.” In <i>AIP Conference Proceedings</i>, 020052-1-020052–54. Leiden, The Netherlands: AIP Publishing, 2019. <a href=\"https://doi.org/10.1063/1.5090019\">https://doi.org/10.1063/1.5090019</a>."},"department":[{"_id":"34"},{"_id":"819"}],"language":[{"iso":"eng"}],"place":"Leiden, The Netherlands","page":"020052-1-020052-4","date_updated":"2023-10-16T13:30:43Z","title":"Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization","publication":"AIP Conference Proceedings","abstract":[{"lang":"eng","text":"There is a range of phenomena in continuous, global multi-objective optimization, that cannot occur in single-objective optimization. For instance, in some multi-objective optimization problems it is possible to follow continuous paths of gradients of straightforward weighted scalarization functions, starting from locally efficient solutions, in order to reach globally Pareto optimal solutions. This paper seeks to better characterize multimodal multi-objective landscapes and to better understand the transitions from local optima to global optima in simple, path-oriented search procedures."}]}