{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T06:43:55Z","timestamp":1740120235784,"version":"3.37.3"},"reference-count":32,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","funder":[{"DOI":"10.13039\/501100009103","name":"Education Department of Shaanxi Province","doi-asserted-by":"publisher","award":["16JK1381"],"award-info":[{"award-number":["16JK1381"]}],"id":[{"id":"10.13039\/501100009103","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Xi'an Weiyang District Science and Technology Bureau","award":["201852"],"award-info":[{"award-number":["201852"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2020,6,15]]},"abstract":"<jats:p> The thorniest difficulties for multi-objective evolutionary algorithms (MOEAs) handling many-objective optimization problems (MaOPs) are the inefficiency of selection operators and high computational cost. To alleviate such difficulties and simplify the MaOPs, objective reduction algorithms have been proposed to remove the redundant objectives during the search process. However, those algorithms can only be applicable to specific problems with redundant objectives. Worse still, the Pareto solutions obtained by reduced objective set may not be the Pareto solutions of the original MaOPs. In this paper, we present a novel objective grouping evolutionary algorithm (OGEA) for general MaOPs. First, by dividing original objective set into several overlapping lower-dimensional subsets in terms of interdependence correlation information, we aim to separate the MaOPs into a number of sub-problems so that each of them can be able to preserve as much dominance structure in the original objective set as possible. Subsequently, we employ the nondominated sorting genetic algorithm II (NSGA-II) to generate Pareto solutions. Besides, instead of nondominated sorting on the whole population, a novel dual selection mechanism is proposed to choose individuals either having high ranks in subspaces or locating sparse region in the objective space for better proximity and diversity. Finally, we compare the proposed strategy with the other two classical space partition methods on benchmark DTLZ5 (I, M), DTLZ2 and a practical engineering problem. Numerical results show the proposed objective grouping algorithm can preserve more dominance structure in original objective set and achieve better quality of Pareto solutions. <\/jats:p>","DOI":"10.1142\/s0218001420590181","type":"journal-article","created":{"date-parts":[[2019,7,22]],"date-time":"2019-07-22T04:21:42Z","timestamp":1563769302000},"page":"2059018","source":"Crossref","is-referenced-by-count":2,"title":["A Novel Objective Grouping Evolutionary Algorithm for Many-Objective Optimization Problems"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8944-3400","authenticated-orcid":false,"given":"Xiaofang","family":"Guo","sequence":"first","affiliation":[{"name":"School of Science, Xi\u2019an Technological University, Xuefu Road, Weiyang District, Xi\u2019an, Shaanxi, P. R. 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