Governed by: Ministry of Industry and Information Technology of the People's Republic of China
Sponsored by: Northwestern Polytechnical University  Chinese Society Aeronautics and Astronautics
Address: Aviation Building,Youyi Campus, Northwestern Polytechnical University
Aerodynamic Optimization Design Based on Hybrid Optimization Algorithm of Particle Swarm Optimization and Artificial Bee Colony Algorithm
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Northwestern Polytechnical University,Northwestern Polytechnical University,Northwestern Polytechnical University,Northwestern Polytechnical University

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V211.4

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    Abstract:

    Aiming at the problem of global/local search balance ability of modern heuristic intelligent algorithms, a new hybrid global optimization algorithm MABCPSO is presented, which is based on the combination of the particle swarm optimization (PSO) and artificial bee colony algorithm (ABC). As a hybrid algorithm,the dual population evolution strategy and information sharing mechanism are used to combine the PSO and ABC algorithm organically. On the one hand, by using the development capability and volatility of ABC algorithm,to maintain the diversity of the population, to avoid the population into the local optimal; on the other hand the mining capacity of PSO algorithm is used to search the individuals with higher fitness value, and accelerate the convergence speed. Function test results show that MABCPSO algorithm has better optimal ability compared with PSO and ABC algorithm. The algorithm is applied to the aerodynamic optimization design of airfoil, showing a good/local search balance, and achieves good optimization results.

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yangmeihua, xialu, zhangxin, mibaigang. Aerodynamic Optimization Design Based on Hybrid Optimization Algorithm of Particle Swarm Optimization and Artificial Bee Colony Algorithm[J]. Advances in Aeronautical Science and Engineering,2017,8(2):182-189

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History
  • Received:February 11,2017
  • Revised:April 12,2017
  • Adopted:April 19,2017
  • Online: June 09,2017
  • Published: