@inbook{4f61cc14356c478296cc449f02494755,
title = "Hybrid estimation of distribution algorithm for multiobjective knapsack problem",
abstract = "We propose a hybrid estimation of distribution algorithm (MOHEDA) for solving the multiobjective 0/1 knapsack problem (MOKP). Local search based on weighted sum method is proposed, and random repair method (RRM) is used to handle the constraints. Moreover, for the purpose of diversity preservation, a new and fast clustering method, called stochastic clustering method (SCM), is also introduced for mixture-based modelling. The experimental results indicate that MOHEDA outperforms several other state-of-the-art algorithms.",
author = "Hui Li and Qingfu Zhang and Edward Tsang and Ford, \{John A.\}",
year = "2004",
doi = "10.1007/978-3-540-24652-7\_15",
language = "英语",
isbn = "3540213678",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "145--154",
editor = "Jens Gottlieb and Raidl, \{Gunther R.\}",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}