A novel multi-objective topology optimization method is developed by simultaneously considering the diversity and uniformity of the optimum solutions in the objective and design variable spaces. To guarantee the diversity of the solutions, a configuration-based clustering scheme is developed and applied to avoid similar designs. By clustering Pareto optimal designs during the optimization process, the searching region in the objective space is gradually reduced, and the time cost required for optimization can be decreased. Additionally, the uniformity of the solutions in the objective space is considered using an adaptive weight determination scheme. The results of the benchmark problems confirm that using the proposed method could reduce the time cost. Furthermore, the overall Pareto front and configuration of different designs are also explored.

通过同时考虑目标和设计变量空间中最优解的多样性和一致性，开发了一种新的多目标拓扑优化方法。为了保证解决方案的多样性，开发并应用了一种基于配置的聚类方案以避免类似的设计。通过在优化过程中对帕累托最优设计进行聚类，逐步缩小目标空间中的搜索区域，降低优化所需的时间成本。此外，使用自适应权重确定方案考虑目标空间中解的均匀性。基准问题的结果证实，使用所提出的方法可以减少时间成本。此外，总体还探讨了不同设计的帕累托前沿和配置。