Ship Pipe Routing Design Using NSGA-II and Coevolutionary Algorithm

Abstract

Pipe route design plays a prominent role in ship design. Due to the complex configuration in layout space with numerous pipelines, diverse design constraints, and obstacles, it is a complicated and time-consuming process to obtain the optimal route of ship pipes. In this article, an optimized design method for branch pipe routing is proposed to improve design efficiency and to reduce human errors. By simplifying equipment and ship hull models and dividing workspace into three-dimensional grid cells, the mathematic model of layout space is constructed. Based on the proposed concept of pipe grading method, the optimization model of pipe routing is established. Then an optimization procedure is presented to deal with pipe route planning problem by combining maze algorithm (MA), nondominated sorting genetic algorithm II (NSGA-II), and cooperative coevolutionary nondominated sorting genetic algorithm II (CCNSGA-II). To improve the performance in genetic algorithm procedure, a fixed-length encoding method is presented based on improved maze algorithm and adaptive region strategy. Fuzzy set theory is employed to extract the best compromise pipeline from Pareto optimal solutions. Simulation test of branch pipe and design optimization of a fuel piping system were carried out to illustrate the design optimization procedure in detail and to verify the feasibility and effectiveness of the proposed methodology.

Ship Pipe Route Design Using Improved A* Algorithm and Genetic Algorithm

“…When routing pipes with different diameter values, the layout space will be decomposed into grids based on the minimum pipe diameter which is equal to the side length of a grid. When routing a pipe p with a larger diameter, the obstacles in the space should be extended outward by a certain distance in advance, which can be calculated by formula (1) [32].…”

“…Dong et al [29,30] used co-evolutionary PSO and GA with fixed-length encoding to route ship pipes. Sui et al [31][32][33] used maze algorithm to create high-quality chromosomes for GA, whereas Wang et al [34,35] generated artificial chromosomes with the mechanism of human-computer cooperation to improve GA and ACO.…”

Pipe route optimization to avoid undesired vibration by using JuliaFEM

“…Particle swarm optimization is also a method that is useful in finding the global minimum for a multi-objective and multi-constrained optimization problem [19]. Pareto optimization provides solutions for optimization problems with multiple goals, such as pipe route optimization [20]. Ant colony algorithms have been applied to classical multi-objective optimization with the weighted sum approach, human-computer cooperation improved optimization and a biobjective automated distributed system design method [21][22][23].…”

Cooperative Route Planning for Multiple Aircraft in a Semifree ATC System

“…Swarm Intelligence Algorithms. Many swarm intelligence algorithms have been developed for solving the route planning problem, including the genetic algorithm (GA) [10], evolutionary programming (EP) [11,12], the Particle Swarm Optimization Algorithm (PSO) [13], and ant colony optimization (AOC) [14,15]. These algorithms are highly robust and flexible and can be used to solve different types of optimization problems; however, the computation speed limits its application in route planning for multiple aircraft.…”