Design optimization of industrial structures is of great importance for engineers in order to provide a cost-effective structural design. Meanwhile, pipe rack is a skeletal industrial structure subjected to various types of loading such as gravity, seismic, piping, and thermal forces. While there are many studies on design optimization of the most common structures, only a limited number of studies exist on optimal design of industrial structures. In this article, a design optimization problem is proposed for weight minimization of steel pipe rack structures, and then the problem is solved through three meta-heuristic algorithms consisting of a modified particle swarm optimization (PSO), grey wolf optimizer (GWO), and the recently developed improved grey wolf optimizer (IGWO). The optimization problem is in discrete form in order to consider practically available cross-sections for the structural members. Stress ratio, drift, and dimensional constraints are imposed during the optimization. In order to demonstrate the capability and effectiveness of the present design optimization problem, a pipe rack structure is optimized by the proposed algorithms, and the optimized designs are compared to an ordinary design in terms of the structural weight and the status of constraints.

为了提供具有成本效益的结构设计，工业结构的设计优化对于工程师而言非常重要。同时，管架是经受各种类型的载荷（例如重力，地震，管道和热力）的骨架工业结构。尽管有很多关于最常见结构的设计优化的研究，但是关于工业结构的优化设计的研究却很少。本文提出了一种设计优化问题，以最小化钢管架结构的重量，然后通过三种元启发式算法解决该问题，包括改进的粒子群算法（PSO），灰狼优化器（GWO）和最近开发的改进的灰太狼优化器（IGWO）。优化问题是离散形式的，以便考虑结构构件的实际可用横截面。在优化过程中会施加应力比，漂移和尺寸约束。为了证明当前设计优化问题的能力和有效性，通过提出的算法对管架结构进行了优化，并将优化后的设计在结构重量和约束状态方面与普通设计进行了比较。