Dynamic Location Search for Identifying Maximum Weighted Independent Sets in Complex Networks

TL;DR

This paper presents DynLS, a novel heuristic algorithm for efficiently solving the NP-hard maximum weighted independent set problem, with applications in intelligent transportation systems, outperforming existing methods in solution quality and speed.

Contribution

The paper introduces DynLS, a new algorithm combining adaptive perturbation, dynamic region adjustment, and variable neighborhood descent to effectively solve MWIS in large, complex networks.

Findings

DynLS outperforms five leading algorithms on 360 test instances.

It finds the best solution for 350 instances within 1000 seconds.

DynLS matches the convergence speed of the best existing algorithm.

Abstract

While Artificial intelligence (AI), including Generative AI, are effective at generating high-quality traffic data and optimization solutions in intelligent transportation systems (ITSs), these techniques often demand significant training time and computational resources, especially in large-scale and complex scenarios. To address this, we introduce a novel and efficient algorithm for solving the maximum weighted independent set (MWIS) problem, which can be used to model many ITSs applications, such as traffic signal control and vehicle routing. Given the NP-hard nature of the MWIS problem, our proposed algorithm, DynLS, incorporates three key innovations to solve it effectively. First, it uses a scores-based adaptive vertex perturbation (SAVP) technique to accelerate convergence, particularly in sparse graphs. Second, it includes a region location mechanism (RLM) to help escape local optima by dynamically adjusting the search space. Finally, it employs a novel variable neighborhood descent strategy, ComLS, which combines vertex exchange strategies with a reward mechanism to guide the search toward high-quality solutions. Our experimental results demonstrate DynLS's superior performance, consistently delivering high-quality solutions within 1000 seconds. DynLS outperformed five leading algorithms across 360 test instances, achieving the best solution for 350 instances and surpassing the second-best algorithm, Cyclic-Fast, by 177 instances. Moreover, DynLS matched Cyclic-Fast's convergence speed, highlighting its efficiency and practicality. This research represents a significant advancement in heuristic algorithms for the MWIS problem, offering a promising approach to aid AI techniques in optimizing intelligent transportation systems.