Improved dynamic adaptive ant colony optimization algorithm to solve pipe routing design

With the purpose of finding a satisfactory pipe path between the starting point and target point, pipe routing design (PRD) has been applied in many industry fields. The research of two-dimensional PRD is the foundation of solving complex RPD problems, and has widely applications in factory layout, facilities installation, and so on. The ant colony optimization (ACO) algorithm is one of the most widely used approaches to solve PRD. However, the traditional ACO has drawbacks such as slow convergence speed, easy to fall into local optimum and low efficiency. In this study, an improved dynamic adaptive ACO (IDAACO) is proposed. The IDAACO includes four novel mechanisms which are the heuristic strategy with direction information, adaptive pseudorandom transfer strategy, improved local pheromone updating mechanism and improved global pheromone updating mechanism. Then, a series of experiments are carried out to verify the effectiveness of the four proposed mechanisms included by IDAACO. Subsequently, the IDAACO is compared with several existing approaches for solving PRD, and the experimental results confirm the advantages of IDAACO in terms of the practicality and high-efficiency. Finally, the IDAACO is used to solve the PRD problem for semi-submersible production platform in oil and gas industry. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

Pipe routing design (PRD) is an important problem in many industry fields, and the ant colony optimization (ACO) algorithm is a commonly used method for solving PRD. This study proposes an improved dynamic adaptive ACO (IDAACO) algorithm and verifies its effectiveness and advantages in solving PRD problems through experiments.