When solving real-world problems with complex simulations, utilizing stochastic algorithms integrated with a simulation model appears inefficient. In this study, we compare several hybrid algorithms for optimizing an offshore jacket substructure (JSS). Moreover, we propose a novel hybrid algorithm called the divisional model genetic algorithm (DMGA) to improve efficiency. By adding different methods, namely particle swarm optimization (PSO), pattern search (PS) and targeted mutation (TM) in three subpopulations to become “divisions,” each division has unique functionalities. With the collaboration of these three divisions, this method is considerably more efficient in solving multiple benchmark problems compared with other hybrid algorithms. These results reveal the superiority of DMGA in solving structural optimization problems.

中文翻译：

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混合算法提高海上风电外套结构优化计算效率

当用复杂的模拟解决现实世界中的问题时，利用与模拟模型集成的随机算法显得效率低下。在这项研究中，我们比较了几种优化海上护套子结构（JSS）的混合算法。此外，我们提出了一种新颖的混合算法，称为分部模型遗传算法（DMGA），以提高效率。通过在三个亚群中添加不同的方法（即粒子群优化（PSO），模式搜索（PS）和目标突变（TM））成为“部门”，每个部门都具有独特的功能。通过这三个部门的协作，与其他混合算法相比，该方法在解决多个基准问题方面效率更高。这些结果表明DMGA在解决结构优化问题方面的优势。