A feasible path-based branch and bound algorithm for strongly nonconvex MINLP problems - Liu, Chao; Ma, Yingjie; Zhang, Dongda; Li, Jie (Frontiers in Chemical Engineering, 2022)
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. The main advantage of this novel algorithm, compared 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 simulation. During B&B, the solution from a parent node is used to initialize the NLP subproblems at the child nodes to improve computational efficiency. The capability of the proposed algorithm is illustrated by solving three dividing wall column optimisation problems and two distillation sequence synthesis problems using rigorous models.