一种改进的果蝇优化算法及其在气动优化设计中的应用

doi:10.7527/S1000-6893.2016.0198

Abstract

Abstract:

As a new swarm intelligence optimization algorithm, fruit fly optimization algorithm (FOA) has a good property of global convergence. In order to further improve the searching performance of FOA and use it for aerodynamic optimization design, a new algorithm named improved fruit fly optimization algorithm (IFOA) is presented. The search step is modified by introducing an inertia weight function to IFOA, and the dynamical balance between the global and the local search is satisfied. The searching efficiency and accuracy of algorithm is integrally improved. For multi-dimensional problems, only one decision variant is randomly changed for producing a new solution in each search, and then a new individual fruit fly is produced to give a search by combining all excellent individuals in the iteration. The convergence speed can thus be greatly accelerated. Function test results show that IFOA has obviously improved the searching performance of FOA. IFOA is applied to aerodynamic optimization design, and the examples of airfoil inverse design and single/multi-objective optimization design demonstrate that IFOA is a simple and efficient optimization method, and can be widely used in aerodynamic optimization design.

Key words: fruit fly optimization algorithm, smell concentration, search step, airfoil, aerodynamic optimization design

CLC Number:

V211.4

TIAN Xu, LI Jie. An improved fruit fly optimization algorithm and its application in aerodynamic optimization design[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2017, 38(4): 120370-120370.

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An improved fruit fly optimization algorithm and its application in aerodynamic optimization design

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