In this article, a high-fidelity structural optimization framework is developed by combining the high-accuracy of finite element method with iterative improvement capability of metaheuristic algorithms. Rotary inertia and transverse shear deformation are included in the finite element model by considering first-order shear deformation theory (FSDT). Three powerful nature-inspired metaheuristic algorithms viz. genetic algorithm (GA) in its classical form, a particle swarm optimization (PSO) variant and a cuckoo search (CS) variant are used. Advanced memetic attributes are incorporated in the PSO and CS to form their respective variants—repulsive particle swarm optimization with local search and chaotic perturbation (RPSOLC) and CHP co-evolutionary host–parasite (CHP). Extensive numerical simulations are carried out to validate these approaches by comparing with existing literature. A comprehensive set of benchmark solutions on certain new problems are also reported. Statistical tests and keen assessment of the predicted results show CHP comprehensively outperforms RPSOLC and GA, while RPSOLC has marginal superiority over GA.

中文翻译：

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使用元启发式算法优化斜复合层压板的频率

本文结合有限元方法的高精度和元启发式算法的迭代改进能力，开发了一种高保真结构优化框架。通过考虑一阶剪切变形理论（FSDT），旋转惯性和横向剪切变形被包含在有限元模型中。三种强大的自然启发式元启发式算法，即。遗传算法 (GA) 的经典形式，使用粒子群优化 (PSO) 变体和布谷鸟搜索 (CS) 变体。高级模因属性被纳入 PSO 和 CS 以形成它们各自的变体——具有局部搜索和混沌扰动的排斥粒子群优化 (RPSOLC) 和 CHP 共同进化宿主-寄生虫 (CHP)。通过与现有文献进行比较，进行了广泛的数值模拟以验证这些方法。还报告了针对某些新问题的一套综合基准解决方案。统计测试和对预测结果的敏锐评估表明，CHP 综合性能优于 RPSOLC 和 GA，而 RPSOLC 略优于 GA。