Hidden-Variables Genetic Algorithm for Variable-Size Design Space Optimal Layout Problems with Application to Aerospace Vehicles

TL;DR

This paper introduces a novel genetic algorithm with hidden-variables for optimizing variable-size component layouts in complex aerospace systems, effectively handling large design spaces and architectural choices.

Contribution

It presents an extended formulation of layout optimization as a variable-size design space problem and develops a hidden-variables genetic algorithm to efficiently solve it.

Findings

The proposed algorithm outperforms traditional methods in complex aerospace layout problems.

It successfully manages large design spaces with multiple architectural options.

Results demonstrate improved optimization performance on real-world aerospace cases.

Abstract

The optimal layout of a complex system such as aerospace vehicles consists in placing a given number of components in a container in order to minimize one or several objectives under some geometrical or functional constraints. This paper presents an extended formulation of this problem as a variable-size design space (VSDS) problem to take into account a large number of architectural choices and components allocation during the design process. As a representative example of such systems, considering the layout of a satellite module, the VSDS aspect translates the fact that the optimizer has to choose between several subdivisions of the components. For instance, one large tank of fuel might be placed as well as two smaller tanks or three even smaller tanks for the same amount of fuel. In order to tackle this NP-hard problem, a genetic algorithm enhanced by an adapted hidden-variables mechanism is proposed. This latter is illustrated on a toy case and an aerospace application case representative to real world complexity to illustrate the performance of the proposed algorithms. The results obtained using the proposed mechanism are reported and analyzed.