基于流形结构重建的多目标气动优化算法

doi:10.7527/S1000-6893.2019.23687

Abstract

Abstract: The Pareto set of a multi-objective design problem is a piecewise continuous k-dimensional manifold, and this fact has always been neglected by traditional multi-objective genetic algorithms. A multi-objective optimization algorithm based on manifold reconstruction is proposed in this paper. The manifold reconstruction algorithm is employed for building the mapping between the design space and the objective space, and the probability distribution of the solution set is built. Then the manifold structure in the objective space is extended, enabling the advancing of the solution set in the objective space to optimize the algorithm. The analytic design cases show that the proposed algorithm is adaptive to problems with diverse Pareto structure features, and the optimization efficiency is improved significantly. The proposed algorithm is also verified by multi-objective aerodynamic design problems. The results demonstrated that about 80% computational cost can be saved compared with traditional multi-objective genetic algorithms. The proposed algorithm has the ability to significantly shorten the aerodynamic design cycle.

Key words: manifold, aerodynamic design, estimation of distribution, multi-objective, Pareto set

CLC Number:

V211.3

SONG Chao, LI Weibin, ZHOU Zhu, LIU Hongyang, LAN Qingsheng. Multi-objective aerodynamic optimization algorithm based on manifold reconstruction[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(5): 623687-623687.

References 25

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Multi-objective aerodynamic optimization algorithm based on manifold reconstruction

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