AI-based 3D pipe automation layout with enhanced ant colony optimization algorithm

Pipe automation layout (PAL) is an important part of the system and has been widely used in many fields. To address the shortcomings of traditional ant colony optimization (ACO) algorithm that tend to fall into local optimum, slow convergence and initial stagnation in three-dimensional (3D) PAL, a variant of ACO called improved multiple strategy ACO (IMSACO) is proposed in this paper. The IMSACO mainly includes four mechanisms: improved heuristic search mechanism with multiple strategies, adaptive pseudorandom state transfer probability strategy, dynamic local pheromone update mechanism, and improved global pheromone update rule based on the wolf pack allocation concept. Then, a series of experiments in 3D environment are conducted to confirm the effectiveness of the presented mechanisms. Subsequently, the IMSACO is compared with several existing improved ACO algorithms for solving 3D PAL. Finally, the IMSACO is applied to solve the PAL problems for offshore production platform in oil processing system.