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.