This paper presents a new methodology for complex system design by means of optimisation techniques. Within the Model-based Engineering approach, optimisation algorithms are used to explore optimal solutions of highly coupled and nonlinear systems. In such scenario, the optimal technology has to be identified and its settings have to be optimised. Relying on optimisation strategies for both the challenges brings to complex mixed-variable problem formulations involving continuous, integer and categorical parameters. Furthermore, part of the parameters are required only if certain technologies are adopted, bringing to variable-size formulations that standard optimisers cannot manage. Therefore, the proposed methodology relies on the use of variable-size mixed-variable global optimiser Structured-Chromosome Genetic Algorithm (SCGA). The advantages of this new method are shown by applying it for solving a space system preliminary design. In particular, two variants have been implemented distinguished by two different levels of complexity. To better appreciate the proposed approach, the same problems have been reformulated to be treated by a well known and appreciated optimiser in the field of spacecraft design, Multi- Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA). The final results of the two approaches are compared and commented.

This paper addresses the problem of including the choice of the High-Lift Devices (HLDs) configuration as a decision variable of an automatic optimisation tool. This task requires the coupling of an estimation routine and an optimisation algorithm. For the former, SU2 flow solver has been used. The Structured-Chromosome Genetic Algorithm (SCGA) optimiser has been employed to search for the optimal HLD. SCGA can overcome the limitations dictated by standard fixed-size continuous optimisation algorithms. Indeed, using hierarchical formulations, it can manage configurational decisions that are conventionally the responsibility of expert designers. The search algorithm bases its strategy on revised genetic operators conceived for handling hierarchical search spaces. The presented research not only shows the practicability of delegating to a specialised optimisation algorithm the complete HLD design but is intended to be a proof of concept for the whole field of multidisciplinary design optimisation. Indeed, the aerospace sector as a whole would benefit by reducing human intervention from the decision process.

Real-world problems such as computational fluid dynamics simulations and finite element analyses are computationally expensive. A standard approach to mitigating the high computational expense is Surrogate-Based Optimization (SBO). Yet, due to the high-dimensionality of many simulation problems, SBO is not directly applicable or not efficient. Reducing the dimensionality of the search space is one method to overcome this limitation. In addition to the applicability of SBO, dimensionality reduction enables easier data handling and improved data and model interpretability. Regularization is considered as one state-of-the-art technique for dimensionality reduction. We propose a hybridization approach called Regularized-Surrogate-Optimization (RSO) aimed at overcoming difficulties related to high-dimensionality. It couples standard Kriging-based SBO with regularization techniques. The employed regularization methods are based on three adaptations of the least absolute shrinkage and selection operator (LASSO). In addition, tree-based methods are analyzed as an alternative variable selection method. An extensive study is performed on a set of artificial test functions and two real-world applications: the electrostatic precipitator problem and a multilayered composite design problem. Experiments reveal that RSO requires significantly less time than standard SBO to obtain comparable results. The pros and cons of the RSO approach are discussed, and recommendations for practitioners are presented.

This paper addresses the problem of autonomous scheduling of space objects' observations from a network of tracking stations to enhance the knowledge of their orbit while respecting allocated resources. This task requires the coupling of a state estimation routine and an optimisation algorithm. As for the former, a sequential filtering approach to estimate the satellite state distribution conditional on received indirect measurements has been employed. To generate candidates, i.e. observation campaigns, a Structured-Chromosome Genetic Algorithm optimiser has been developed, which is able to address the issue of handling mixed-discrete global optimisation problems with variable-size design space. The search algorithm bases its strategy on revised genetic operators that have been reformulated for handling hierarchical search spaces. The presented approach aims at supporting the space sector by tracking both operational satellites and non-collaborative space debris in response to the challenge of a constantly increasing population size in the near Earth environment. The potential of the presented methodology is shown by solving the optimisation of a tracking window schedule for a very low Earth satellite operating in a highly perturbed dynamical environment.

Surrogate Model Based Optimization (SMBO) is an established technique for handling computationally expensive optimization problems. One important application is the optimization of Particle Reinforced Metal Matrix Composites (PRMMCs). Multi-phase materials are gaining attention. Their performance is strongly affected by microscale properties. By optimizing the microscale structure, these materials can be tailored to satisfy specific requirements. Current manufacturing techniques have limited control over the distribution of reinforcing particles and are subject to considerable uncertainty. Moreover, the simulation and optimization of PRMMCs requires significant computational effort. We propose an approach that tackles the problem of optimizing the characteristics of PRMMCs subject to uniaxial load, by improving the particles' spatial distribution. The optimization problem is split into a bilevel problem: The upper-level optimization aims to find the particle distribution parameters which maximize the PRMMC limit load. Due to potentially infeasible distributions, the lower-level problem attempts to create a particle placement that reflects the specifications of an upper-level candidate solution.