Evolutionary ant colony optimization with dual-factor heuristics for scalable path planning in IIoT-enabled logistics systems

Path planning constitutes a fundamental theoretical challenge in the scheduling and management of intelligent industrial vehicles within Industrial Internet of Things (IIoT)-enabled logistics frameworks. While the Ant Colony Optimization (ACO) algorithm is widely employed for such combinatorial problems, conventional implementations often encounter high computational latency, stagnant search efficiency, and susceptibility to deadlocks. This study proposes an improved evolutionary ant colony algorithm specifically engineered for the dynamic constraints of intelligent industrial vehicle scheduling. We introduce a dual-strategy framework: a simplified mode mechanism to accelerate exploration in low-complexity regions and a dual-factor heuristic function to mitigate deadlock risks. By incorporating evolutionary principles into the pheromone update rules, the algorithm achieves accelerated convergence toward Pareto-optimal solutions. Comparative experimental analysis against Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and classical ACO demonstrates that the proposed approach reduces multi-objective function values by 23.5–59.2%. Our findings indicate that the algorithm effectively balances task completion time and operational costs under strict deadline constraints, providing a scalable solution for high-throughput IIoT applications.