The Industrial Internet of Things (IIoT) is characterized by the instantaneous communication of diverse critical data across complex, resource-limited networks. To maximize the efficiency and accuracy of task scheduling in such networks, this study introduces a novel task scheduling framework based on the Adaptive Lotus Effect Optimization Algorithm (ALEOA). ALEOA perfectly combines the self-cleaning mechanism and learning function of the Lotus effect with the global search and learning capabilities of the Particle Swarm Optimization (PSO) algorithm to establish equilibrium between local and global search. ALEOA can adaptively adjust routing and task scheduling to support critical data communication and a dynamic network. Results from experiments on typical benchmarking equations and simulations of the IIoT communication environment show that the proposed ALEOA-based model yields higher task acceptance rates, lower communication delays, and improved resource utilization than traditional metaheuristic methods. Statistical analyses using the Wilcoxon Signed Rank test confirmed the effectiveness of this enhancement.
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