基于粒子群和人工蜂群混合算法的气动优化设计

现代启发式智能算法存在全局与局部搜索能力的平衡问题,针对此问题,采用双种群进化策略和信息交流机制,提出一种基于粒子群算法和人工蜂群算法相结合的新型混合优化算法——MABCPSO,并分别进行函数测试和翼型的气动优化设计验证。结果表明:MABCPSO新型混合优化算法具有更好的寻优能力,相比粒子群算法和人工蜂群算法,该算法能以更少的进化代数分别提高1.7%和2.2%的减阻效果。

Aerodynamic Optimization Design Based on Hybrid Optimization Algorithm of Particle Swarm Optimization and Artificial Bee Colony Algorithm

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.