Plate structures are the integral parts of any maritime engineering platform. With the recent focus on composite structures, the need for optimizing their design and functionality has now been tremendously realized. In this paper, a comprehensive study is carried out on the effectiveness and optimization performance of three metaheuristic algorithms in designing skew composite laminates under dynamic operational environments. The natural frequencies of the composite panels are measured using a first-order shear deformation theory-based finite element (FE) approach. The stacking sequence of the composite panels is optimized so that the natural frequency separation between the first two modes is maximized. The three metaheuristics considered here are genetic algorithm (GA), repulsive particle swarm optimization with local search and chaotic perturbation (RPSOLC), and co-evolutionary host-parasite (CHP) algorithm. It is observed that in general, the FE-coupled metaheuristic algorithms are quite capable to significantly improve the baseline designs. In particular, FE-CHP algorithm outperforms both the FE-GA and FE-RPSOLC algorithms with respect to accuracy, computational speed and solution reliability.

板结构是任何海事工程平台的组成部分。随着近来对复合结构的关注，已经极大地意识到了优化其设计和功能的需求。本文对三种元启发式算法在动态运行环境下设计斜交复合材料层压板的有效性和优化性能进行了全面研究。复合板的固有频率是使用基于一阶剪切变形理论的有限元（FE）方法测量的。优化了复合面板的堆叠顺序，从而使前两种模式之间的固有频率间隔最大化。这里考虑的三种元启发法是遗传算法（GA），带有局部搜索和混沌扰动（RPSOLC）的排斥粒子群优化算法，以及协同进化宿主-寄生虫（CHP）算法。可以观察到，通常来说，有限元耦合的元启发式算法完全有能力显着改善基线设计。特别是，FE-CHP算法在准确性，计算速度和解决方案可靠性方面均优于FE-GA和FE-RPSOLC算法。