Although there are many algorithms that can solve the multi-objective optimization problems (MOPs) efficiently, each algorithm has its own disadvantages. The emergence of new algorithms is beneficial to make up the deficiencies of existing algorithms. Inspired by the organic matter transport process and the branch update theory of the banyan, this work proposed a new bio-inspired algorithm, named the multi-objective artificial tree (MOAT) algorithm to solve the MOPs. In MOAT, an improved crossover operator and an improved self-evolution operator are introduced to update solutions, a adaptive grid method is applied to manage the non-dominated solutions, and the strategy of variable number of branches in population is adopted to enhance the accuracy of this algorithm. Many typical test functions and seven well-known multi-objective algorithms, including MOEAD, NSGAII, MOPSO, GDE3, εMOEA, IBEA and MPSO/D, are applied to study the accuracy and efficiency of MOAT. Experimental tests show that the results of MOAT are better than those of the seven algorithms, and the performance of MOAT is demonstrated. In addition, this new algorithm is also applied to solve the MOPs of two-dimensional acoustic metamaterials (AMs). The key parameters of AMs are optimized by MOAT to mitigate impact load and reduce structural mass, and the performance of these AMs is significantly improved.

中文翻译：

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多目标人工树（MOAT）算法的开发及其在声学超材料中的应用

尽管有许多算法可以有效地解决多目标优化问题（MOP），但每种算法都有其自身的缺点。新算法的出现有利于弥补现有算法的不足。受有机物运输过程和印度榕树分支更新理论的启发，这项工作提出了一种新的生物启发算法，称为多目标人工树（MOAT）算法来解决MOP。在MOAT中，引入了改进的交叉算子和改进的自进化算子来更新解，应用自适应网格方法管理非支配解，并采用了种群中分支数可变的策略来提高精度。该算法。许多典型的测试功能和七种著名的多目标算法，包括MOEAD，NSGAII，MOPSO，GDE3，εMOEA，IBEA和MPSO / D，用于研究MOAT的准确性和效率。实验测试表明，MOAT的结果优于七个算法，并证明了MOAT的性能。此外，该新算法还用于求解二维声学超材料（AM）的MOP。MOAT优化了AM的关键参数，以减轻冲击载荷并减少结构质量，并且显着改善了这些AM的性能。该新算法还用于求解二维声学超材料（AM）的MOP。MOAT优化了AM的关键参数，以减轻冲击载荷并减少结构质量，并且显着提高了这些AM的性能。该新算法还用于解决二维声学超材料（AM）的MOP。MOAT优化了AM的关键参数，以减轻冲击载荷并减少结构质量，并且显着提高了这些AM的性能。