{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T16:56:55Z","timestamp":1648918615501},"reference-count":19,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2011,9]]},"abstract":"<jats:p> In many optimization problems in practice, multiple objectives have to be optimized at the same time. Some multi-objective problems are characterized by multiple connected Pareto-sets at different parts in decision space \u2014 also called equivalent Pareto-subsets. We assume that the practitioner wants to approximate all Pareto-subsets to be able to choose among various solutions with different characteristics. In this work, we propose a clustering-based niching framework for multi-objective population-based approaches that allows to approximate equivalent Pareto-subsets. Iteratively, the clustering process assigns the population to niches, and the multi-objective optimization process concentrates on each niche independently. Two exemplary hybridizations, rake selection and DBSCAN, as well as SMS-EMOA and kernel density clustering demonstrate that the niching framework allows enough diversity to detect and approximate equivalent Pareto-subsets. <\/jats:p>","DOI":"10.1142\/s1469026811003112","type":"journal-article","created":{"date-parts":[[2011,10,10]],"date-time":"2011-10-10T16:00:33Z","timestamp":1318262433000},"page":"295-311","source":"Crossref","is-referenced-by-count":2,"title":["A CLUSTERING-BASED NICHING FRAMEWORK FOR THE APPROXIMATION OF EQUIVALENT PARETO-SUBSETS"],"prefix":"10.1142","volume":"10","author":[{"given":"OLIVER","family":"KRAMER","sequence":"first","affiliation":[{"name":"Department of Computer Science, Carl von Ossietzky University Oldenburg, Uhlhornsweg 84, 26111 Oldenburg, Germany"}]},{"given":"HOLGER","family":"DANIELSIEK","sequence":"additional","affiliation":[{"name":"TU Dortmund, Department of Computer Science, Otto-Hahn-Str. 14, 44221 Dortmund, Germany"}]}],"member":"219","published-online":{"date-parts":[[2012,4,30]]},"reference":[{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2006.08.008"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1023\/A:1015059928466"},{"key":"rf6","series-title":"Genetic and Evolutionary Computation Series","volume-title":"Evolutionary Algorithms for Solving Multi-Objective Problems","author":"Coello C. 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