基于DMOM算法的航空发动机性能寻优控制

提出一种分散迁移优化算法(DMOM),可实现多峰值优化问题的全局最优解搜索.该算法通过随机选择参考粒子,不断迁移搜索自身所处区域峰值点,再通过分散操作排除局部最优点,重新生成新个体,可快速搜索到全局最优区域.将DMOM应用于航空发动机性能寻优控制仿真,结果表明:在最小油耗和最低涡轮温度模式下, DMOM的寻优速度相比遗传算法(GA)和粒子群算法(PSO)提高了2倍以上;同时DMOM的优化精度相比自组织迁移算法(SOMA)提高了60%以上,相比可行性序列二次规划(FSQP)算法提高了20%以上.验证了DMOM相比其他优化算法有更强的跳出局部最优的能力,在航空发动机最小油耗和最低涡轮温度这类多峰值寻优问题中具有明显的优势. 

Aero-engine performance seeking control based on DMOM algorithm

A distributed migration optimization method (DMOM) for multi-peak optimization problem was proposed to find global optima solution. In this algorithm, the local optima could be searched out by continuous discrete and migration with new individuals. Then the global optima solution was found by sorting. During the discrete process the possibility of sinking into local optima was reduced compared with other algorithms. The DMOM was used for aero-engine performance seeking control in digital simulation. Results show that, in minimum fuel mode and minimum turbine temperature mode, the DMOM is improved by more than 2 times compared with genetic algorithm (GA) and particle swarm optimization (PSO), the optimization accuracy of DMOM is improved by more than 60% compared with self-organizing migrating algorithm (SOMA), and more than 20% compared with feasible sequential quadratic programming(FSQP). Comparative results show that the calculation time can be reduced and the aero-engine performance can be improved by using this algorithm.