Multimodality in Multi-Objective Optimization — More Boon than Bane? Grimme, Christian Kerschke, Pascal Trautmann, Heike Deb, Kalyanmoy Goodman, Erik Coello, Coello Carlos A. Klamroth, Kathrin Miettinen, Kaisa Mostaghim, Sanaz Reed, Patrick This paper addresses multimodality of multi-objective (MO) optimization landscapes. Contrary to common perception of local optima, according to which they are hindering the progress of optimization algorithms, it will be shown that local efficient sets in a multi-objective setting can assist optimizers in finding global efficient sets. We use sophisticated visualization techniques, which rely on gradient field heatmaps, to highlight those insights into landscape characteristics. Finally, the MO local optimizer MOGSA is introduced, which exploits those observations by sliding down the multi-objective gradient hill and moving along the local efficient sets. Springer 2019 info:eu-repo/semantics/conferenceObject doc-type:conferenceObject text http://purl.org/coar/resource_type/c_5794 https://ris.uni-paderborn.de/record/46343 Grimme C, Kerschke P, Trautmann H. Multimodality in Multi-Objective Optimization — More Boon than Bane? In: Deb K, Goodman E, Coello CCA, et al., eds. <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i>. Vol 11411. Lecture Notes in Computer Science. Springer; 2019:126–138. doi:<a href="https://doi.org/10.1007/978-3-030-12598-1_11">10.1007/978-3-030-12598-1_11</a> eng info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-12598-1_11 info:eu-repo/semantics/closedAccess