Feasible Path-Based Branch and Bound Algorithm for Highly Nonconvex Mixed-Integer Nonlinear Programming Problems

In this paper, a feasible path-based branch and bound (B&B) algorithm is presented for solving mixed-integer nonlinear programming problems with highly nonconvex nature. The main advantage of this novel algorithm, comparing to the conventional branch and bound algorithms, is that when solving a...

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Vydáno v:Chemical engineering transactions Ročník 94
Hlavní autoři: Chao Liu, Yingjie Ma, Dongda Zhang, Jie Li
Médium: Journal Article
Jazyk:angličtina
Vydáno: AIDIC Servizi S.r.l 01.09.2022
ISSN:2283-9216
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Abstract In this paper, a feasible path-based branch and bound (B&B) algorithm is presented for solving mixed-integer nonlinear programming problems with highly nonconvex nature. The main advantage of this novel algorithm, comparing to the conventional branch and bound algorithms, is that when solving a nonlinear programming (NLP) subproblem at each node, our previously proposed hybrid steady-state and time-relaxation-based optimisation algorithm is employed. This approach allows circumventing complex initialisation procedure and overcoming the convergence difficulties of process simulations. During B&B, the solution from a parent node is used to initialize the NLP subproblems at the child nodes to improve the efficiency of this algorithm. The capability of the proposed algorithm is illustrated by solving a dividing wall column optimisation case for separation of a ternary mixture. The optimal design is obtained in 2, 712 CPU s with TAC 43,344 $ y(1.
AbstractList In this paper, a feasible path-based branch and bound (B&B) algorithm is presented for solving mixed-integer nonlinear programming problems with highly nonconvex nature. The main advantage of this novel algorithm, comparing to the conventional branch and bound algorithms, is that when solving a nonlinear programming (NLP) subproblem at each node, our previously proposed hybrid steady-state and time-relaxation-based optimisation algorithm is employed. This approach allows circumventing complex initialisation procedure and overcoming the convergence difficulties of process simulations. During B&B, the solution from a parent node is used to initialize the NLP subproblems at the child nodes to improve the efficiency of this algorithm. The capability of the proposed algorithm is illustrated by solving a dividing wall column optimisation case for separation of a ternary mixture. The optimal design is obtained in 2, 712 CPU s with TAC 43,344 $ y(1.
Author Chao Liu
Yingjie Ma
Jie Li
Dongda Zhang
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