A Memory-Guided Hybrid Artificial Bee Colony Algorithm with Variable Neighborhood Search for Green Power Consumption Optimization in Long-Distance Oil Pipelines

This paper addresses high electricity costs in long-distance crude oil transmission systems due to limited renewable energy integration. A mixed-integer linear programming (MILP) model is formulated to maximize renewable energy use and minimize purchased electricity costs, considering pump station and pipeline constraints. To solve this problem, a hybrid artificial bee colony algorithm with variable neighborhood search (HABC-VNS) is proposed, incorporating memory guidance, discrete uniform crossover, and three neighborhood structures. The algorithm is compared with standard ABC, binary PSO, and GA. In two experimental setups (four pumps/eight stations, 24 h horizon), HABC-VNS achieves average total costs of 33,000 CNY and 43,000 CNY, respectively, compared to 61,000–368,000 CNY for the other methods. Average green power integration rates reach 61.6% and 59.6%, outperforming all baselines. The proposed approach provides effective scheduling under strong operational constraints.