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Syst."],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>When solving expensive multi-objective optimization problems, surrogate models are often used to reduce the number of true evaluations. Based on predictions from the surrogate models, promising candidate solutions, also referred to as infill solutions, can be identified for evaluation to expedite the search towards the optimum. This infill process in turn involves optimization of certain criteria derived from the surrogate models. In this study, predicted hypervolume maximization is considered as the infill criterion for expensive multi\/many-objective optimization. In particular, we examine the effect of normalization bounds on the performance of the algorithm building on our previous study on bi-objective optimization. We propose a more scalable approach based on \u201csurrogate corner\u201d search that shows improved performance where some of the conventional techniques face challenges. Numerical experiments on a range of benchmark problems with up to 5 objectives demonstrate the efficacy and reliability of the proposed approach.<\/jats:p>","DOI":"10.1007\/s40747-021-00590-9","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T05:10:55Z","timestamp":1638853855000},"page":"1193-1209","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Adjusting normalization bounds to improve hypervolume based search for expensive multi-objective optimization"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0431-3437","authenticated-orcid":false,"given":"Bing","family":"Wang","sequence":"first","affiliation":[]},{"given":"Hemant Kumar","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Tapabrata","family":"Ray","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"590_CR1","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.tcs.2011.03.012","volume":"425","author":"A Auger","year":"2012","unstructured":"Auger A, Bader J, Brockhoff D, Zitzler E (2012) Hypervolume-based multiobjective optimization: theoretical foundations and practical implications. 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