Identifying multiple vulnerable areas of infrastructure network under global connectivity measure

Infrastructure networks provide significant services for our society. Nevertheless, high dependence on physical infrastructures makes infrastructure networks vulnerable to disasters or intentional attacks which being considered as geographically related failures that happened in specific geographical locations and result in failures of neighboring network components. To provide comprehensive network protection against failures, vulnerability of infrastructure network needs to be assessed with various network performance measures. However, when considering about multiple vulnerable areas, available researches just employ measure of total number of affected edges while neglecting edges’ different topologies. In this paper, we focus on identifying multiple vulnerable areas under global connectivity measure: Size Ratio of the Giant Component (SRGC). Firstly, Deterministic Damage Circle Model and Multiple Barycenters Method are presented to determine damage impact and location of damage region. For solving the HP-hard problem of identifying multiple optimal attacks, we transform the problem into combinational optimization problem and propose a mixed heuristic strategy consisted of both Greedy Algorithm and Genetic Algorithm to attain the optimal solution. We obtain numerical results for real-world infrastructure network, thereby demonstrating the effectiveness and applicability of the presented strategy and algorithms. The distinctive results of SRGC indicate the necessity and significance of considering global connectivity measure in assessing vulnerability of infrastructure networks.

New gradient methods for sensor selection problems

In this article, we consider the sensor selection problem of choosing [Formula: see text] sensors from a set of [Formula: see text] possible sensor measurements. The sensor selection problem is a combinational optimization problem. Evaluating the performance for each possible combination is impractical unless [Formula: see text] and [Formula: see text] are small. We relax the original selection problem to be a convex optimization problem and describe a projected gradient method with Barzilai–Borwein step size to solve the proposed relaxed problem. Numerical results demonstrate that the proposed algorithm converges faster than some classical algorithms. The solution obtained by the proposed algorithm is closer to the truth.

Numerical and Experimental Study on the Multiobjective Optimization of a Two-Disk Flexible Rotor System

With the ever-increasing requirement for the thrust to weight ratio, the rotational speed of modern aeroengine is increasingly improved; thus most of the aeroengine rotor is flexible. Some dynamic problems, such as excessive vibration, appear due to the increase of the rotation speed of the aeroengine. The aim of this study is to reduce the vibration level of the flexible rotor system through optimum design. A laboratory scale two-disk flexible rotor system representing a typical aeroengine rotor system is designed. A combinational optimization strategy coupling the rotordynamics calculation software ANSYS and the multidisciplinary optimization software ISIGHT is proposed to optimize the rotor system. The positions of the disks are selected as the design variables. Constraints are imposed on critical speeds. The disks’ amplitudes and bearings’ transmitted forces are chosen as the optimization objectives. Using this strategy, the optimal positions of the two disks are obtained. The numerical optimization results are verified by the experiments based on the test rig. The results show a significant vibration level reduction after optimization.

Turn-Based War Chess Model and Its Search Algorithm per Turn

War chess gaming has so far received insufficient attention but is a significant component of turn-based strategy games (TBS) and is studied in this paper. First, a common game model is proposed through various existing war chess types. Based on the model, we propose a theory frame involving combinational optimization on the one hand and game tree search on the other hand. We also discuss a key problem, namely, that the number of the branching factors of each turn in the game tree is huge. Then, we propose two algorithms for searching in one turn to solve the problem: (1) enumeration by order; (2) enumeration by recursion. The main difference between these two is the permutation method used: the former uses the dictionary sequence method, while the latter uses the recursive permutation method. Finally, we prove that both of these algorithms are optimal, and we analyze the difference between their efficiencies. An important factor is the total time taken for the unit to expand until it achieves its reachable position. The factor, which is the total number of expansions that each unit makes in its reachable position, is set. The conclusion proposed is in terms of this factor: Enumeration by recursion is better than enumeration by order in all situations.