<p>The process of planning and setting up a system of pipes and associated parts as efficiently and effectively as feasible in order to satisfy particular operational objectives and limitations is known as optimizing piping layouts. Optimizing this particular problem aims to achieve the best overall design by balancing a number of elements such as the cost reduction, operational efficiency, space use, and structural integrity. In this paper, two hierarchical criteria are minimized (i) the piping distance (to reduce the cost), and (ii) the fluid pressure drop (to increase efficiency). Indeed, there are many real-world industrial applications such as installing oil, gas, and water pipelines, where flow stability is required, as well as a reduction in the piping length. To optimize these two criteria, an innovative artificial intelligence method namely Dhouib-Matrix-SPP2-4 (DM-SPP2-4) is designed and developed under Python programming language. DM-SPP2-4 uses four movement directions and ergonomically visualizes the designed piping layout in a grid map environment. The performance of DM-SPP2-4 is proved on different oil and gas case studies and compared to several implemented artificial intelligence methods developed in the literature such as the Improved Dynamic Adaptive Ant Colony Optimization Algorithm, the Improved Ant Colony Optimization, the Improved Particle Swarm Optimization and the Ant Colony Algorithm and its derivatives. The simulation results demonstrate that DM-SPP2-4 can rapidly design the optimal piping route based on the shortest distance and maximal fluids pressure. Moreover, DM-SPP2-4 finds novel optimal solutions that other artificial intelligence methods cannot generate. In addition, for all the instances DM-SPP2-4 is the fastest artificial intelligence technique with a significant variance.</p>