Development of a multi-objective artificial tree (MOAT) algorithm and its application in acoustic metamaterials

Qiqi Li, Zhichen He, Quan Bing Eric Li, Tao Chen, Qiuyu Wang, Aiguo Cheng

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Although there are many algorithms that can solve the multi-objective optimization problems(MOPs)efficiently, each algorithm has its own disadvantages. The emergenceof new algorithms is beneficial to make up the deficienciesof existing algorithms.Inspired bythe organic matter transport process and the branch update theory of the banyan, this work proposeda new bio-inspired algorithm,namedthe multi-objective artificialtree (MOAT)algorithmto solve the MOPs. In MOAT, an improved crossover operatorand an improved self-evolutionoperatorare introducedto update solutions, aadaptive gridmethodis applied to manage the non-dominated solutions, andthe strategy of variable number of branches in population is adopted to enhance the accuracyof thisalgorithm.Manytypical test functionsand seven well-known multi-objective algorithms, including MOEAD, NSGAII,MOPSO, GDE3, εMOEA, IBEAand MPSO/D,are applied to study the accuracy and efficiencyof MOAT.Experimental tests show that the results of MOAT are better than those of the seven algorithms, and the performance of MOATis demonstrated.In addition, thisnew algorithmis also appliedto solve the MOPs of two-dimensionalacoustic metamaterials (AMs).The key parameters ofAMs areoptimizedby MOAT to mitigate impact load and reduce structuralmass, and the performance of these AMs issignificantly improved.
Original languageEnglish
Number of pages32
JournalMemetic Computing
Publication statusAccepted/In press - 4 May 2020


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