Hybrid Approach for Multi-Objective Combinatorial Optimization in Search of Greener Aircraft

Abstract

Pursuing “greener aircraft” with lower emissions and noise than today’s commercial transport aircraft has become an important effort across government, industry and academia. A commonly held perspective of pursuing greener aircraft is that a broad suite of new technologies and, potentially, new aircraft configurations provide the means to attaining a greener aircraft rather than incremental improvements to existing designs. Determining the appropriate combination, or portfolio, of technologies requires a method that can both sort through the myriad possible combinations of available technologies and aircraft configurations along with determining the size and dimensions of the best aircraft for a given selection of technologies. Characterizing an aircraft as “greener” requires consideration of several different metrics (e.g. carbon emissions – as measured by fuel burn, NOx emissions) along with basic economic considerations (e.g. required yield or ticket price). This combination of features makes this a multi-objective aircraft design optimization problem with both discrete and continuous design variables. This paper presents a hybrid multi-objective algorithm and demonstrates its ability to find solutions for a constrained multi-objective mixed discrete nonlinear programming problem. The algorithm hybridizes Genetic Algorithm with a gradient-based sequential quadratic programming algorithm in a manner that seems to overcome the demerits of these two algorithms when used independently. Applied to the greener aircraft problem, the algorithm seeks to arrive at the best trade-offs between representative environmental and economic metrics. The paper also describes the aircraft sizing tool used to evaluate the objective function values. While the detail and fidelity of the aircraft sizing model limits the quality of the results, the application suggests that the hybrid algorithm does have promise to assist decision-makers in choosing the appropriate technology portfolio.