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Content Provider | ACM Digital Library |
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Author | Halgamuge, Saman Kumara Kirley, Michael Sun, Yuan |
Abstract | Cooperative co-evolution is a framework that can be used to effectively solve large scale optimization problems. This approach employs a divide and conquer strategy, which decomposes the problem into sub-components that are optimized separately. However, solution quality relies heavily on the decomposition method used. In recent years, a number of decomposition methods have been proposed, which raises another research question: Which decomposition method is best for a given large scale optimization problem? In this paper, we focus on the selection of the best decomposition method for large scale fully non-separable problems. Four decomposition methods are compared on a suite of benchmark functions. We observe that the random grouping method obtains the best solution quality on the benchmark large scale fully non-separable problems. |
Starting Page | 1213 |
Ending Page | 1216 |
Page Count | 4 |
File Format | |
ISBN | 9781450334884 |
DOI | 10.1145/2739482.2768483 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2015-07-11 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Algorithm selection Cooperative co-evolution Large scale global optimization Problem decomposition |
Content Type | Text |
Resource Type | Article |
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