A layout system design for ship engine room and equipment by artificial intelligence

This study aims at the problems of traditional intelligent algorithms, such as being prone to getting trapped in local optima, having slow convergence speeds, and insufficient accuracy when solving complex optimisation problems. It systematically compares and analyses the advantages and limitations of the genetic algorithm (GA), particle swarm optimisation (PSO) algorithm, and ant colony algorithm (ACA). Moreover, a GA-PSO-ACA that combines the advantages of the three is proposed. Taking the layout design of ship engine room equipment as the research object, under the consideration of multiple constraints, such as no overlap of equipment, balanced weight distribution, operation space, and safety distance, three traditional algorithms and the GA-PSO-ACA are respectively used for simulation optimisation. The results show that the optimal objective function value of the GPA algorithm is 160, which is significantly better than that of the GA (220), the PSO algorithm (225), and the ACA (218).