Research on performance optimization of adaptive cycle engine based on improved multi-objective particle swarm optimization

An improved multi-objective comprehensive learning particle swarm optimization algorithm was proposed by changing the method of crowding distance in this paper. Then this paper adjusts the selection method of global optimal particle and introduced self-adaptive mutation operator. The test result shows that the efficiency of improved optimization algorithm proposed by this paper is significantly increased. A performance simulation model for adaptive cycle engine (ACE) was established in this paper. And the ACE performance optimization problem, considering with two objections: maximum thrust at design point and minimum install specific fuel consumption at subsonic cruise, is solved by improved optimization algorithm. Noninferior solutions within constrains of the engine were obtained, which indicated the optimized regularities of parameter-matching at design point and component-adjusting at off design point for installed performance of ACE.