Satellite-based platforms are currently the only feasible way of achieving intercontinental range for quantum communication, enabling thus the future global quantum internet. Recent demonstrations by the Chinese spacecraft Micius have spurred an international space race and enormous interest in the development of both scientific and commercial systems. Research efforts so far have concentrated upon in-orbit demonstrations involving a single satellite and one or two ground stations. Ultimately satellite quantum key distribution should enable secure network communication between multiple nodes, which requires efficient scheduling of communication with the set of ground stations. Here we present a study of how satellite quantum key distribution can service many ground stations taking into account realistic constraints such as geography, operational hours, and most importantly, weather conditions. The objective is to maximise the number of keys a set of ground stations located in the United Kingdom could share while simultaneously reflecting the communication needs of each node and its relevance in the network. The problem is formulated as a mixed-integer linear optimisation program and solved to a desired optimality gap using a state of the art solver. The approach is presented using a simulation run throughout six years to investigate the total number of keys that can be sent to ground stations.

This paper proposes a multi-population adaptive version of inflationary differential evolution algorithm. Inflationary differential evolution algorithm (IDEA) combines basic differential evolution (DE) with some of the restart and local search mechanisms of Monotonic Basin Hopping (MBH). In the adaptive version presented in this paper, the DE parameters CR and F are automatically adapted together with the size of the local restart bubble and the number of local restarts of MBH. The proposed algorithm implements a simple but effective mechanism to avoid multiple detections of the same local minima. The novel mechanism allows the algorithm to decide whether to start or not a local search. The algorithm has been extensively tested over more than fifty test functions from the competitions of the Congress on Evolutionary Computation (CEC), CEC 2005, CEC 2011 and CEC 2014, and compared against all the algorithms participating in those competitions. For each test function, the paper reports best, worst, median, mean and standard deviation values of the best minimum found by the algorithm. Comparisons with other algorithms participating in the CEC competitions are presented in terms of relative ranking, Wilcoxon tests and success rates. For completeness, the paper presents also the single population adaptive IDEA, that can adapt only CR and F, and shows that this simpler version can outperform the multi-population one if the radius of the restart bubble and the number of restarts are properly chosen. IntroductionDifferential evolution (DE), proposed by Price et al. (2006), is a well-known population-based evolutionary algorithm for solving global optimisation problems over continuous spaces. Literature indicates that DE exhibits very good performance over a wide variety of optimisation problems (Das and Suganthan 2011). However, although being a very efficient optimiser, its local search ability has long been questioned and work has been done to improve its local con-Communicated by V. Loia.

In this paper a Multi-Population Inflationary Differential Evolution algorithm with Adaptive Local Restart is presented and extensively tested over more than fifty test functions from the CEC 2005, CEC 2011 and CEC 2014 competitions. The algorithm combines a multi-population adaptive Differential Evolution with local search and local and global restart procedures. The proposed algorithm implements a simple but effective mechanism to avoid multiple detections of the same local minima. The novel mechanism allows the algorithm to decide whether to start or not a local search. The local restart of the population, which follows the local search, is, therefore, automatically adapted.

In this paper two strategies are proposed to de-orbit up to 10 noncooperative objects per year from the region within 800 and 1400 km altitude in Low Earth Orbit (LEO). The underlying idea is to use a single servicing spacecraft to de-orbit several objects applying two different approaches. The first strategy is analogous to the Traveling Salesman Problem: the servicing spacecraft rendezvous with multiple objects in order to physically attach a de-orbiting kit that reduces the perigee of the orbit. The second strategy is analogous to the Vehicle Routing Problem: the servicing spacecraft rendezvous and docks with an object, spirals it down to a lower altitude orbit, undocks, and then spirals up to the next target.In order to maximise the number of de-orbited objects with minimum propellant consumption, an optimal sequence of targets is identified using a bio-inspired incremental automatic planning and scheduling discrete optimisation algorithm. The optimisation of the resulting sequence is realised using a direct transcription method based on an asymptotic analytical solution of the perturbed Keplerian motion. The analytical model takes into account the perturbations deriving from the J 2 gravitational effect and the atmospheric drag.

Atira asteroids are recently-discovered celestial bodies characterised by orbits lying completely inside the heliocentric orbit of the Earth. The study of these objects is difficult due to the limitations of ground-based observations: objects can only be detected when the Sun is not in the field of view of the telescope. However, many asteroids are expected to exist in the inner region of the Solar System, many of which could pose a significant threat to our planet. In this paper, a small, low-cost, mission to visit the known Atira asteroids and to discover new Near Earth Asteroids (NEA) is proposed. The mission is realised using electric propulsion. The trajectory is optimised

This paper proposes a novel approach to the solution of optimal control problems under uncertainty (OCPUUs). OCPUUs are first cast in a general formulation that allows the treatment of uncertainties of different nature, and then solved with a new direct transcription method that combines multiple shooting with generalised polynomial algebra to model and propagate extended sets.The continuity conditions on extended sets at the boundary of two adjacent segments are directly satisfied by a bounding approach. The Intrusive Polynomial Algebra aNd Multiple shooting Approach (IPANeMA) developed in this work can handle optimal control problems under a wide range of uncertainty models, including nonparametric, epistemic, and imprecise probability ones. In this paper, the approach is applied to the design of a robust low-thrust trajectory to a Near-Earth Object with uncertain initial conditions. It is shown that the new method provides more robust and reliable trajectories than the solution of an analogous deterministic optimal control problem.

This work presents some preliminary results on the low-thrust tour of the main asteroid belt. The asteroids are visited through a series of fly-by's that minimise the total cost of the manoeuvres. The sequence of asteroids to visit and the initial orbit for the spacecraft are chosen based on the Minimum Orbit Intersection Distance (MOID) between the orbit of the asteroids and the orbit of the spacecraft. The transfers between asteroids are designed using a low-thrust analytical model that provides nearly optimal solutions with coast and thrust arcs. The mission analysis is completed with a study of the transfer of the spacecraft from the Earth to the first orbit of the tour. Number of revolutions on the intermediate phasing orbit

This work presents an analysis of the deployment of future constellations using a combination of low-thrust propulsion and natural dynamics. Different strategies to realise the transfer from the launcher injection orbit to the constellation operational orbit are investigated. The deployment of the constellation is formulated as a multi-objective optimisation problem that aims at minimising the maximum transfer ∆V , the launch cost and maximise at the same time the pay-off given by the service provided by the constellation. The paper will consider the case of a typical constellation with 27 satellites in Medium Earth Orbit and the use of only two launchers, one of which can carry a single satellite. It will be demonstrated that some strategies and deployment sequences are dominant and provide the best trade-off between peak transfer ∆V and monetary pay-off.