Due to the substantial volume of materials used in the structural members of jacket platforms, weight optimization is of critical importance. A Genetic Algorithm is a powerful tool that allows for consideration of many parameters in the optimum design of platforms simultaneously, a feature hardly found in ordinary circumstances. Regarding the cyclic loads derived from waves, fatigue is one of the most fundamental issues in the design and optimization of offshore platforms. In this study, using the Genetic Algorithm, a real platform is optimized with two optimization scenarios named (CF) and (WCF). The CF-Optimization considers constraints, including fatigue life in connections, stress and buckling design requirements in structural members, horizontal displacement at the working point level, and structural adequacy control of connections, while the WCF-Optimization ignores fatigue constraint in the mentioned constraints. The results show that (CF) and (WCF) optimizations reduced the structural weight of the platform by the ratios of 13% and 15%, respectively. Moreover, an investigation of the results obtained from WCF-Optimization reveals that ignoring fatigue could significantly lead to unreliable results in the design optimization of the jacket platform.

由于在外套平台的结构构件中使用了大量材料，因此重量优化至关重要。遗传算法是一种功能强大的工具，可以在平台的最佳设计中同时考虑许多参数，这在通常情况下很难找到。对于波浪引起的周期性载荷，疲劳是海上平台设计和优化中最基本的问题之一。在这项研究中，使用遗传算法，使用名为（CF）和（WCF）的两个优化方案对真实平台进行了优化。CF-Optimization考虑了各种约束，包括连接的疲劳寿命，结构构件的应力和屈曲设计要求，工作点水平的水平位移以及连接的结构充分性控制，而WCF-Optimization在提到的约束中忽略了疲劳约束。结果表明（CF）和（WCF）优化使平台的结构重量分别减少了13％和15％。此外，对从WCF-Optimization获得的结果的调查表明，忽略疲劳可能会极大地导致夹克平台设计优化中不可靠的结果。