An efficient convexification method for solving generalized geometric problems

Hao Chun Lu*

*此作品的通信作者

研究成果: 期刊稿件文章同行評審

10 引文 斯高帕斯(Scopus)

摘要

Convexification transformation is vital for solving Generalized Geometric Problems (GGP) in global optimization. Björk et al. [3] posited that transforming non-convex signomial terms in a GGP into 1-convex functions is currently the most efficient convexification technique. However, to the best of our knowledge, an efficient convexification method based on the concept of 1-convex functions has not been proposed. To address this research gap, we present a Beta method to maximally improve the efficiency of convexification based on the concept of 1-convex functions, and thereby enhance the accuracy of linearization without increasing the number of break points and binary variables in the piecewise linear function. The Beta method yields an excellent solution quality and computational efficiency. We compare its performance, with that of three existing approaches using four numerical examples. The computational results demonstrate that, in terms of solution quality and computation time, the proposed method outperforms the compared approaches.

原文英語
頁(從 - 到)429-455
頁數27
期刊Journal of Industrial and Management Optimization
8
發行號2
DOIs
出版狀態已出版 - 05 2012
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