A hybrid ant colony-particle swarm optimization method (ACOPSO) for aerospace propulsion systems

Abstract

Purpose The purpose of this paper is to introduce a hybrid, metaheuristic, multimodal and multi-objective optimization tool that is needed for aerospace propulsion engineering problems. Design/methodology/approach Multi-objective hybrid optimization code is integrated with various benchmark and test functions that are selected suitable to the difficulty level of the aero propulsion performance problems. Findings Ant colony and particle swarm optimization (ACOPSO) has performed satisfactorily with benchmark problems. Research limitations/implications ACOPSO is able to solve multi-objective and multimodal problems. Because every optimization problem has specific features, it is necessary to search their general behavior using other algorithms. Practical implications In addition to the optimization solving, ACOPSO enables an alternative methodology for turbine engine performance calculations by using generic components maps. The user is flexible for searching various effects of component designs along with the compressor and turbine maps. Originality/value A hybrid optimization code that has not been used before is introduced. It is targeted use is propulsion systems optimization and design such as Turboshaft or turbofan by preparing the necessary engine functions. A number of input parameters and objective functions can be modified accordingly.