Bipartite graphs are widely used to capture the relationships between two types of entities. In bipartite graph analysis, finding the maximum balanced biclique (MBB) is an important problem with numerous applications. A biclique is balanced if its two disjoint vertex sets are of equal size. However, in real-world scenarios, each vertex is associated with a weight to denote its properties, such as influence, i.e., weighted bipartite graph. For weighted bipartite graphs, the previous studies for MBB are no longer applicable due to the ignorance of weight. To fill the gap, in this paper, we propose a reasonable definition of “balance” by restricting the weight difference between two sides of a biclique within $k$. Given a weighted bipartite graph $G$ and a constraint $k$, we aim to find the maximum $k$-balanced biclique (Max $k$ BB) with the maximum weight. To address the problem, we first propose an approach based on biclique enumeration on single side of $G$ following the Branch-and
Information flow topology plays a crucial role in the control of connected autonomous vehicles. This paper proposes an approach to search for the Pareto optimal information flow topology off-line for the control of connected vehicles platoon using a non-dominated sorting genetic algorithm. Based on the obtained Pareto optimal information flow topology, the platoons overall performance in terms of three main performance indices: tracking index, acceleration standard deviation, and fuel consumption, are all improved. Numerical simulations are used to validate the effectiveness of the proposed approach. In the simulation, the impact of different information flow topologies on the performance of the connected autonomous vehicles platoon is firstly investigated. The results show that more communication links can lead to better tracking ability. The smoothness of the velocity profile is consistent with fuel economy, while velocity profiles smoothness, fuel economy and communication efficienc