Destruction resistance optimization of chemical material network based on discrete space binary ABC algorithm

Abstract

Optimizing destruction resistance of chemical material networks can reduce the occurrence and propagation of cascading failures. Most destructive resistance indexes only assess the final degree of destructive paralysis, making it difficult to accurately identify weak links. To solve this problem, toughness index is introduced. However, because it is an NP-complete problem that lacks a polynomial-time solution and cannot achieve automatic optimization and autonomous decision-making, the optimization is not guaranteed to be optimal. In order to solve the above problems, this article proposes a binary artificial bee colony algorithm (binary ABC algorithm) based on discrete space for optimizing cascade failures resistance model. Firstly, a fitness function is designed based on toughness theory. Then improve the honey source generation and update mechanisms in the ABC algorithm, and transform the search space into D-dimensional binary space, the toughness and cut point set of the network are obtained by simulation. Finally, the proposed method is compared with other optimization methods and attack strategies respectively, to determine the weak nodes in the chemical material network and optimize its destruction resistance. The case study shows that the model is feasible and can automatically select optimal attacks to identify weak links that need to be protected. The value of the destruction resistance indicator increased from 0.2748 to 0.5909, providing a theoretical basis for cascading fault analysis and prevention in chemical material networks.