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Proceedings Paper

BMI optimization by using parallel UNDX real-coded genetic algorithm with Beowulf cluster
Author(s): Masaya Handa; Michihiro Kawanishi; Hiroshi Kanki
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Paper Abstract

This paper deals with the global optimization algorithm of the Bilinear Matrix Inequalities (BMIs) based on the Unimodal Normal Distribution Crossover (UNDX) GA. First, analyzing the structure of the BMIs, the existence of the typical difficult structures is confirmed. Then, in order to improve the performance of algorithm, based on results of the problem structures analysis and consideration of BMIs characteristic properties, we proposed the algorithm using primary search direction with relaxed Linear Matrix Inequality (LMI) convex estimation. Moreover, in these algorithms, we propose two types of evaluation methods for GA individuals based on LMI calculation considering BMI characteristic properties more. In addition, in order to reduce computational time, we proposed parallelization of RCGA algorithm, Master-Worker paradigm with cluster computing technique.

Paper Details

Date Published: 9 January 2008
PDF: 8 pages
Proc. SPIE 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 67940T (9 January 2008); doi: 10.1117/12.784340
Show Author Affiliations
Masaya Handa, Kobe Univ. (Japan)
Michihiro Kawanishi, Toyota Technological Institute (Japan)
Hiroshi Kanki, Kobe Univ. (Japan)

Published in SPIE Proceedings Vol. 6794:
ICMIT 2007: Mechatronics, MEMS, and Smart Materials
Minoru Sasaki; Gisang Choi Sang; Zushu Li; Ryojun Ikeura; Hyungki Kim; Fangzheng Xue, Editor(s)

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