In this article, a multi-objective topology optimization (MOTO) methodology based on the hybridization of the Non-dominated Sorting Genetic Algorithm II (NSGAII) and Differential Evolutionary (DE) algorithm is proposed. The framework of the proposed hybrid multiobjective optimization (MOO) algorithm is elaborated, and its performances and advantages over existing standard MOO methods are evaluated and demonstrated by solving typical mathematical test functions. To validate the proposed hybrid MOTO methodology, it is applied to the topology optimization of an electromagnetic actuator. Both linear and nonlinear cases are investigated. The numerical results demonstrate that a set of novel topologies with improved multiple objectives is obtained.

中文翻译：

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一种多目标拓扑优化方法及其在电磁执行器设计中的应用

在本文中，提出了一种基于非支配排序遗传算法 II (NSGAII) 和差分进化 (DE) 算法混合的多目标拓扑优化 (MOTO) 方法。阐述了所提出的混合多目标优化（MOO）算法的框架，并通过求解典型的数学测试函数来评估和证明其性能和优于现有标准 MOO 方法的优势。为了验证所提出的混合 MOTO 方法，将其应用于电磁致动器的拓扑优化。研究了线性和非线性情况。数值结果表明，获得了一组具有改进的多目标的新型拓扑。