Search Results (11)

Illegal, unreported, and unregulated (IUU) fishing threatens marine ecosystems in the Western Pacific. Conventional patrol strategies under-utilize the available multi-source surveillance data. This study proposes a maritime patrol-routing framework that integrates AIS fishing effort, Sentinel-1 SAR dark-vessel detections, and GFW vessel encounter records into a Surveillance Priority Index (SPI) over the study domain (0–20° N, 140–160° E). An Adaptive Priority-Boosted Ant Colony Optimization (APB-ACO) algorithm with two-phase deadline-aware route construction and best-of-N adaptive strategy selection produces patrol routes that cover high-priority cells within a 72 h window while minimizing total distance. Across 30 random seeds and a benchmark suite (PB-ACO, GA, PSO, DQN, NSGA-II), APB-ACO yields the shortest mean route ($\mathrm{21,658}\pm 9$ km, $7\%$ shorter than PB-ACO, $p<0.001$), the lowest variance ($46\times$ lower standard deviation than PB-ACO), and 100% high-priority coverage at default settings; a scalability analysis across 2–20% high-priority task ratios shows that the coverage gap over PB-ACO widens with the HP ratio. The problem is also formalized as a Mixed-Integer Linear Program (Priority-Constrained VRPTW), positioning APB-ACO as a constructive metaheuristic for an NP-hard operational problem. The framework’s principal limitation is that, in the tested three-vessel scenario, the 500 km inter-vessel communication constraint is violated more than 1100 times per 72 h mission and is repaired post hoc; integrating this constraint into the optimizer is identified as a near-term extension. The results provide a methodological foundation for surveillance-driven patrol planning rather than a validated tool for operational IUU interdiction. Full article

Network interdiction problems involving edge deletion on shortest paths have wide applications. However, in many practical scenarios, the complete removal of edges is infeasible. The minimum-cost shortest-path interdiction problem for trees with the weighted $l_{\infty }$ norm (MCSPIT_∞) is studied in this paper. The goal is to upgrade selected edges at minimum total cost such that the shortest root–leaf distance is bounded below by a given value. We designed an $O(nlogn)$ algorithm based on greedy techniques combined with a binary search method to solve this problem efficiently. We then extended the framework to the minimum-cost shortest-path double interdiction problem for trees with the weighted $l_{\infty }$ norm, which imposes an additional requirement that the sum of root–leaf distances exceed a given threshold. Building upon the solution to (MCSPIT_∞), we developed an equally efficient $O(nlogn)$ algorithm for this variant. Finally, numerical experiments are presented to demonstrate both the effectiveness and practical performance of the proposed algorithms. Full article

The Max+Sum Spanning Tree (MSST) problem, with applications in secure communication systems, seeks a spanning tree T minimizing ${max}_{e\in T}w\left(e\right)+{\sum }_{e\in T}c\left(e\right)$ on a given edge-weighted undirected network $G(V,E,c,w)$, where the sets V and E are the sets of vertices and edges, respectively. The functions c and w are defined on the edge set, representing transmission cost and verification delay in secure communication systems, respectively. This problem can be solved within $O\left(\right|E|log|V\left|\right)$ time. We investigate its interdiction (MSSTID) and improvement (MSSTIP) problems under the weighted $l_{\infty }$ norm. MSSTID seeks minimal edge weight adjustments (to either c or w) to degrade network performance by ensuring the optimal MSST’s weight is at least K, while MSSTIP similarly aims to enhance performance by making the optimal MSST’s weight at most K through minimal weight modifications. These problems naturally arise in adversarial and proactive performance enhancement scenarios, respectively, where network robustness or efficiency must be guaranteed through constrained resource allocation. We first establish their mathematical models. Subsequently, we analyze the properties of the optimal value to determine the relationship between the magnitude of a given number and the optimal value. Then, utilizing binary search methods and greedy techniques, we design four algorithms with time complexity $O\left(\right|E{|}^{2}log\left|V\right|)$ to solve the above problems by modifying w or c. Finally, numerical experiments are conducted to demonstrate the effectiveness of the algorithms. Full article

Most existing studies on the Shortest-Path Network Interdiction Problem (SPIP) adopt the attacker’s perspective, often overlooking the critical role of defender-oriented strategies. To support proactive defense, this paper introduces a novel problem named the Multi-Objective Shortest-Path Counter-Interdiction Problem (MO-SPCIP). The problem incorporates a backup-based defense strategy from the defender’s viewpoint and addresses the inherent trade-offs among minimizing the shortest path length, minimizing backup resource consumption, and maximizing the attacker’s resource usage. To solve this complex problem, we propose an Improved Pareto Local Search-based Evolutionary Algorithm (IPLSEA). The algorithm integrates several problem-specific components, including a tailored initial solution generation method, a customized solution representation, and specialized genetic operators. In addition, an improved Pareto Local Search (IPLS) is incorporated into the algorithm framework, allowing an adaptive and selective search. To further enhance local refinement, three problem-specific neighborhood search operations are designed and embedded within the Pareto Local Search. The experimental results demonstrate that IPLSEA significantly outperforms state-of-the-art algorithms in terms of its convergence quality and solution diversity, enabling a more robust performance in network counter-interdiction scenarios. Full article

Network security is a crucial challenge facing Internet-of-Things (IoT) systems worldwide, which leads to serious safety alarms and great economic loss. This paper studies the problem of malicious interdicting network exploitation of IoT systems that are modeled as a bi-layer logical–physical network. In this problem, a virtual attack takes place at the logical layer (the layer of Things), while the physical layer (the layer of Internet) provides concrete support for the attack. In the interdiction problem, the attacker attempts to access a target node on the logical layer with minimal communication cost, but the defender can strategically interdict some key edges on the physical layer given a certain budget of interdiction resources. This setting generalizes the classic single-layer shortest-path network interdiction problem, but brings in nonlinear objective functions, which are notoriously challenging to optimize. We reformulate the model and apply Benders decomposition process to solve this problem. A layer-mapping module is introduced to improve the decomposition algorithm and a random-search process is proposed to accelerate the convergence. Extensive numerical experiments demonstrate the computational efficiency of our methods. Full article

This paper studies the cyclic dynamic gaming case of the r-interdiction median problem with fortification (CDGC-RIMF), which is important for strengthening a facility’s reliability and invulnerability under various possible attacks. We formulated the CDGC-RIMF as a bi-objective mixed-integer linear programming (MILP) model with two opposing goals to minimize/maximize the loss from both the designer (leader) and attacker (follower) sides. The first goal was to identify the most cost-effective plan to build and fortify the facility considering minimum loss, whereas the attacker followed the designer to seek the most destructive way of attacking to cause maximum loss. We found that the two sides could not reach a static equilibrium with a single pair of confrontational plans in an ordinary case, but were able to reach a dynamically cyclic equilibrium when the plan involved multiple pairs. The proposed bi-objective model aimed to discover the optimal cyclic plans for both sides to reach a dynamic equilibrium. To solve this problem, we first started from the designer’s side with a design and fortification plan, and then the attacker was able to generate their worst attack plan based on that design. After that, the designer changed their plan again based on the attacker’s plan in order to minimize loss, and the attacker correspondingly modified their plan to achieve maximum loss. This game looped until, finally, a cyclic equilibrium was reached. This equilibrium was deemed to be optimal for both sides because there was always more loss for either side if they left the equilibrium first. This game falls into the subgame of a perfect Nash equilibrium—a kind of complete game. The proposed bi-objective model was directly solved by the CPLEX solver to achieve optimal solutions for small-sized problems and near-optimal feasible solutions for larger-sized problems. Furthermore, for large-scale problems, we developed a heuristic algorithm that implemented dynamic iterative partial optimization alongside MILP (DIPO-MILP), which showed better performance compared with the CPLEX solver when solving large-scale problems. Full article

Time-sensitive transportation systems have received increasing research attention recently. Examples of time-sensitive networks include those of perishable goods, high-value commodity, and express delivery. Much research has been devoted to optimally locating key facilities such as transportation hubs to minimize transit time. However, there is a lack of research attention to the reliability and vulnerability of time-sensitive transportation networks. Such issues cannot be ignored as facilities can be lost due to reasons such as extreme weather, equipment malfunction, and even intentional attacks. This paper proposes a hub interdiction center (HIC) model for evaluating the vulnerability of time-sensitive hub-and-spoke networks under disruptions. The model identifies the set of hub facilities whose loss will lead to the greatest increase in the worst-case transit time. From a planning perspective, such hubs are critical facilities that should be protected or enhanced by preventive measures. An efficient integer linear programming (ILP) formulation of the new model is developed. Computational experiments on a widely used US air passenger dataset show that losing a small number of hub facilities can double the maximum transit time. Full article

Goal recognition (GR) is a method of inferring the goals of other agents, which enables humans or AI agents to proactively make response plans. Goal recognition design (GRD) has been proposed to deliberately redesign the underlying environment to accelerate goal recognition. Along with the GR and GRD problems, in this paper, we start by introducing the goal recognition control (GRC) problem under network interdiction, which focuses on controlling the goal recognition process. When the observer attempts to facilitate the explainability of the actor’s behavior and accelerate goal recognition by reducing the uncertainty, the actor wants to minimize the privacy information leakage by manipulating the asymmetric information and delay the goal recognition process. Then, the GRC under network interdiction is formulated as one static Stackelberg game, where the observer obtains asymmetric information about the actor’s intended goal and proactively interdicts the edges of the network with a bounded resource. The privacy leakage of the actor’s actions about the real goals is quantified by a min-entropy information metric and this privacy information metric is associated with the goal uncertainty. Next in importance, we define the privacy information metric based GRC under network interdiction (InfoGRC) and the information metric based GRC under threshold network interdiction (InfoGRCT). After dual reformulating, the InfoGRC and InfoGRCT as bi-level mixed-integer programming problems, one Benders decomposition-based approach is adopted to optimize the observer’s optimal interdiction resource allocation and the actor’s cost-optimal path-planning. Finally, some experimental evaluations are conducted to demonstrate the effectiveness of the InfoGRC and InfoGRCT models in the task of controlling the goal recognition process. Full article

Shortest-path network interdiction, where a defender strategically allocates interdiction resource on the arcs or nodes in a network and an attacker traverses the capacitated network along a shortest s-t path from a source to a terminus, is an important research problem with potential real-world impact. In this paper, based on game-theoretic methodologies, we consider a novel stochastic extension of the shortest-path network interdiction problem with goal threshold, abbreviated as SSPIT. The attacker attempts to minimize the length of the shortest path, while the defender attempts to force it to exceed a specific threshold with the least resource consumption. In our model, threshold constraint is introduced as a trade-off between utility maximization and resource consumption, and stochastic cases with some known probability p of successful interdiction are considered. Existing algorithms do not perform well when dealing with threshold and stochastic constraints. To address the NP-hard problem, SSPIT-D, a decomposition approach based on Benders decomposition, was adopted. To optimize the master problem and subproblem iteration, an efficient dual subgraph interdiction algorithm SSPIT-S and a local research based better-response algorithm SSPIT-DL were designed, adding to the SSPIT-D. Numerical experiments on networks of different sizes and attributes were used to illustrate and validate the decomposition approach. The results showed that the dual subgraph and better-response procedure can significantly improve the efficiency and scalability of the decomposition algorithm. In addition, the improved enhancement algorithms are less sensitive and robust to parameters. Furthermore, the application in a real-world road network demonstrates the scalability of our decomposition approach. Full article

Large-scale monitoring systems have seen rapid development in recent years. Wireless sensor networks (WSN), composed of thousands of sensing, computing and communication nodes, form the backbone of such systems. Integration with unmanned aerial vehicles (UAVs) leads to increased monitoring area and to better overall performance. This paper presents a hybrid UAV-WSN network which is self-configured to improve the acquisition of environmental data across large areas. A prime objective and novelty of the heterogeneous multi-agent scheme proposed here is the optimal generation of reference trajectories, parameterized after inter- and intra-line distances. The main contribution is the trajectory design, optimized to avoid interdicted regions, to pass near predefined way-points, with guaranteed communication time, and to minimize total path length. Mixed-integer description is employed into the associated constrained optimization problem. The second novelty is the sensor localization and clustering method for optimal ground coverage taking into account the communication information between UAV and a subset of ground sensors (i.e., the cluster heads). Results show improvements in both network and data collection efficiency metrics by implementing the proposed algorithms. These are initially evaluated by means of simulation and then validated on a realistic WSN-UAV test-bed, thus bringing significant practical value. Full article

This paper proposes a reliable network interdiction model with multiple unit costs, which maximizes the minimum arrival cost of the invader to the sink by setting obstacles on some arcs with limited resources in the given network. In other words, given a graph with a source and a sink, several arcs will be selected with limited resources such that each path contains as many weights as possible. This model needs to be transferred into a bilevel program because its constraints can hardly be listed explicitly even for a graph with a moderate size, because the number of paths between any two given points increases exponentially according to the size of the graph. This bilevel model is equivalent to an integer model with a low degree number of constraints by converting the inner programming to a shortest path problem. We first prove that this problem is non-deterministic polynomial-time (NP)-hard. Secondly, we reduce the number of constraints to the first power from the exponential degree by using the dual technique. Lastly, the national railway network is used to show the feasibility of our method. Full article