A multi-objective optimization method for power material supply chain network based on HBA algorithm

The power supply chain network faces pronounced multi-objective optimization challenges. To balance conflicting objectives, this paper introduces an HBA-based multi-objective optimization approach. It integrates transportation, storage, time penalty costs, and optimal material allocation within a unified model. Inspired by honey badgers' hunting tactics and teamwork, the HBA algorithm mimics their olfactory search and mining to conduct global and local exploration of the supply chain. By simulating the honey badgers' search pattern, the algorithm identifies potential optimization points, balancing objectives iteratively to converge on optimal solutions. Experiments confirm that this HBA-driven method enhances supply chain efficiency, reliability, and cost-effectiveness.