Efficient ship pipeline routing with dual-strategy enhanced ant colony optimization: Active behavior adjustment and passive environmental adaptability
Xin Wang, Fengfeng Ning, Zemin Lin, Zhinan Zhang
Abstract
The ship pipeline system is crucial as the transmission pathway for water, oil and gas, of which the layout design directly affects system efficiency, cost and safety. However, multiple objectives and constraints are involved in the large-scale ship pipe routing design problem, so the traditional ant colony algorithm is difficult to fully meet the requirements in terms of search efficiency and solution quality. This research proposes a Dual-Strategy Enhanced Ant Colony Optimization (DEACO) algorithm enhanced by both active and passive strategies. The active strategy, inspired by the behavior patterns of natural ant colonies, includes an adaptive greedy adjustment mechanism, heterogeneous pheromone deposition rule, and self-regulating pheromone secretion mechanism to enhance searching flexibility and efficiency. The passive strategy incorporates endpoint guidance enhancement and dynamic pheromone limits to adjust algorithm response, achieving fast path routing. Cases with two different environment settings show that DEACO outperforms traditional ACO, two latest ACOs and improved PSO in terms of key metrics such as pipe lengths and numbers of bends with faster computation speed. The algorithm achieves high stability within the same scenarios and strong robustness across various scenarios, yielding consistently favorable results despite randomness in searching and condition variations. Therefore, the proposed algorithm demonstrates effectiveness and superiority in ship pipeline automated routing.