A feasible path-based branch and bound algorithm for strongly nonconvex MINLP problems

IF 2.5 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Chao Liu, Yingjie Ma, Dongda Zhang, Jie Li
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引用次数: 0

Abstract

In this paper, a feasible path-based branch and bound (B&B) algorithm is proposed to solve mixed-integer nonlinear programming problems with highly nonconvex nature through integration of the previously proposed hybrid feasible-path optimisation algorithm and the branch and bound method. The main advantage of this novel algorithm is that our previously proposed hybrid steady-state and time-relaxation-based optimisation algorithm is employed to solve a nonlinear programming (NLP) subproblem at each node during B&B. The solution from a parent node in B&B is used to initialize the NLP subproblems at the child nodes to improve computational efficiency. This approach allows circumventing complex initialisation procedure and overcoming difficulties in convergence of process simulation. The capability of the proposed algorithm is illustrated by several process synthesis and intensification problems using rigorous models.
强非凸MINLP问题的可行路径分支定界算法
本文将已有的混合可行路径优化算法与分支定界法相结合,提出了一种基于可行路径的分枝定界(B&B)算法,用于求解高度非凸的混合整数非线性规划问题。这种新算法的主要优点是我们之前提出的混合稳态和基于时间松弛的优化算法用于解决B&B过程中每个节点的非线性规划(NLP)子问题。利用B&B中父节点的解初始化子节点上的NLP子问题,提高计算效率。这种方法可以避免复杂的初始化过程,克服过程模拟收敛的困难。通过使用严格模型的几个过程综合和强化问题说明了该算法的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.50
自引率
0.00%
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0
审稿时长
13 weeks
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