@inbook{f99ff864b0344b4199c4aa7008f157f6,
title = "Rank-based nondomination set identification with preprocessing",
abstract = "In multi-objective optimization, finding a nondomination set is a computationally expensive process and the complexity grows with the popula-tion size. In this paper, we propose a nondomination set identification method with (a) rank-based preprocessing step where the obvious dominant solutions are eliminated and (b) better order of comparison based on the average rank so that the number of comparisons can be significantly reduced. In preprocessing, the maximum rank information of the solutions that are best in each individual objectives is used. In addition, during nondomination set identification process to check if a solution is nondominant the solution is compared only with solutions that are better in terms of average rank. The experiment results demonstrate the effectiveness of the proposed method in identifying the non-dominant set in less number of comparisons.",
keywords = "Multi-objective evolutionary algorithms, Nondomination set identification, Preprocessing step, Rank-based sorting",
author = "Vikas Palakonda and Rammohan Mallipeddi",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.",
year = "2016",
doi = "10.1007/978-3-319-41009-8\_16",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "150--157",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}