An Improved Pareto Local Search-Based Evolutionary Algorithm for Multi-Objective Shortest-Path Network Counter-Interdiction Problem

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.