A direct memetic approach to the solution of multi-objective optimal control problems

This paper proposes a memetic direct transcription algorithm to solve Multi-Objective Optimal Control Problems (MOOCP). The MOOCP is first transcribed into a Non-linear Programming Problem (NLP) with Direct Finite Elements in Time (DFET) and then solved with a particular formulation of the Multi Agent Collaborative Search (MACS) framework. Multi Agent Collaborative Search is a memetic algorithm in which a population of agents combines local search heuristics, exploring the neighbourhood of each agent, with social actions exchanging information among agents. A collection of all Pareto optimal solutions is maintained in an archive that evolves towards the Pareto set. In the approach proposed in this paper, individualistic actions run a local search, from random points within the neighbourhood of each agent, solving a normalised Pascoletti-Serafini scalarisation of the multi-objective NLP problem. Social actions, instead, solve a bi-level problem in which the lower level handles only the constraint equations while the upper level handles only the objective functions. The proposed approach is tested on the multi-objective extensions of two well-known optimal control problems: the Goddard Rocket problem, and the maximum energy orbit rise problem

Multi-objective optimisation of constellation deployment using low-thrust propulsion

This work presents an analysis of the deployment of future constellations using a com-bination 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 ΔV, the launch cost and maximise at the same time the pay-off given by the service provided by the constellation. The paperwill 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 ΔV and monetary pay-off

Global solution of multi-objective optimal control problems with multi agent collaborative search and direct finite elements transcription

This paper addresses the solution of optimal control problems with multiple and possibly conflicting objective functions. The solution strategy is based on the integration of Direct Finite Elements in Time (DFET) transcription into the Multi Agent Collaborative Search (MACS) framework. Multi Agent Collaborative Search is a memetic algorithm in which a population of agents performs a set of individual and social actions looking for the Pareto front. Direct Finite Elements in Time transcribe an optimal control problem into a constrained Non-linear Programming Problem (NLP) by collocating states and controls on spectral bases. MACS operates directly on the NLP problem and generates nearly-feasible trial solutions which are then submitted to a NLP solver. If the NLP solver converges to a feasible solution, an updated solution for the control parameters is returned to MACS, along with the corresponding value of the objective functions. Both the updated guess and the objective function values will be used by MACS to generate new trial solutions and converge, as uniformly as possible, to the Pareto front. To demonstrate the applicability of this strategy, the paper presents the solution of the multi-objective extensions of two well-known space related optimal control problems: the Goddard Rocket problem, and the maximum energy orbit rise problem

Multi-agent quality of experience control

In the framework of the Future Internet, the aim of the Quality of Experience (QoE) Control functionalities is to track the personalized desired QoE level of the applications. The paper proposes to perform such a task by dynamically selecting the most appropriate Classes of Service (among the ones supported by the network), this selection being driven by a novel heuristic Multi-Agent Reinforcement Learning (MARL) algorithm. The paper shows that such an approach offers the opportunity to cope with some practical implementation problems: in particular, it allows to face the so-called “curse of dimensionality” of MARL algorithms, thus achieving satisfactory performance results even in the presence of several hundreds of Agents

An FTIR Microspectroscopy Ratiometric Approach for Monitoring X-ray Irradiation Effects on SH-SY5Y Human Neuroblastoma Cells

The ability of Fourier transform infrared (FTIR) spectroscopy in analyzing cells at a molecular level was exploited for investigating the biochemical changes induced in protein, nucleic acid, lipid, and carbohydrate content of cells after irradiation by graded X-ray doses. Infrared spectra from in vitro SH-SY5Y neuroblastoma cells following exposure to X-rays (0, 2, 4, 6, 8, 10 Gy) were analyzed using a ratiometric approach by evaluating the ratios between the absorbance of significant peaks. The spectroscopic investigation was performed on cells fixed immediately (t0 cells) and 24 h (t24 cells) after irradiation to study both the initial radiation-induced damage and the effect of the ensuing cellular repair processes. The analysis of infrared spectra allowed us to detect changes in proteins, lipids, and nucleic acids attributable to X-ray exposure. The ratiometric analysis was able to quantify changes for the protein, lipid, and DNA components and to suggest the occurrence of apoptosis processes. The ratiometric study of Amide I band indicated also that the secondary structure of proteins was significantly modified. The comparison between the results from t0 and t24 cells indicated the occurrence of cellular recovery processes. The adopted approach can provide a very direct way to monitor changes for specific cellular components and can represent a valuable tool for developing innovative strategies to monitor cancer radiotherapy outcome

A multi-variable DTR algorithm for the estimation of conductor temperature and ampacity on HV overhead lines by IoT data sensors

The transfer capabilities of High-Voltage Overhead Lines (HV OHLs) are often limited by the critical power line temperature that depends on the magnitude of the transferred current and the ambient conditions, i.e., ambient temperature, wind, etc. To utilize existing power lines more effectively (with a view to progressive decarbonization) and more safely with respect to the critical power line temperatures, this paper proposes a Dynamic Thermal Rating (DTR) approach using IoT sensors installed on some HV OHLs located in different Italian geographical locations. The goal is to estimate the OHL conductor temperature and ampacity, using a data-driven thermo-mechanical model with the Bayesian probability approach, in order to improve the confidence interval of the results. This work highlights that it could be possible to estimate a space-time distribution of temperature for each OHL and an increase in the actual current threshold values for optimizing OHL ampacity. The proposed model is validated using the Monte Carlo method

Multidisciplinary design analysis of a semi-reusable two-stage-to-orbit small payload launch system

This paper contains an analysis of the design trade-ofs for a semi-reusable two-stage to orbit, rocket- powered launch system for small payloads. The system is to be air-launched of a modifed commercial aircraft with a full reusable, flight capable return vehicle. This allows the system to be operational from any suitable space or airport; a increasingly common aim of new, agile launch concepts for small payloads. The system was modelled with a multidisciplinary design optimisation (MDO) framework using two different optimal control solvers: MODHOC, a multi-objective optimiser that combined an evolutionary- based global optimisation together with a collocation-based optimal control solver; and TROPICO, a single-objective optimiser using a direct multi-shooting approach

Multi-objective optimal control of ascent trajectories for launch vehicles

This paper presents a novel approach to the solution of multi-objective optimal control problems. The proposed solution strategy is based on the integration of the Direct Finite Elements Transcription method, to transcribe dynamics and objectives, with a memetic strategy called Multi Agent Collaborative Search (MACS). The original multi-objective optimal control problem is reformulated as a bi-level nonlinear programming problem. In the outer level, handled by MACS, trial control vectors are generated and passed to the inner level, which enforces the solution feasibility. Solutions are then returned to the outer level to evaluate the feasibility of the corresponding objective functions, adding a penalty value in the case of infeasibility. An optional single level refinement is added to improve the ability of the scheme to converge to the Pareto front. The capabilities of the proposed approach will be demonstrated on the multi-objective optimisation of ascent trajectories of launch vehicles

NEPTUNE (Nuclear process-driven Enhancement of Proton Therapy UNravEled)

Protontherapy is an important radiation modality that has been used to treat cancer for over 60 years. In the last 10 years, clinical proton therapy has been rapidly growing with more than 80 facilities worldwide [1]. The interest in proton therapy stems from the physical properties of protons allowing for a much improved dose shaping around the target and greater healthy tissue sparing. One shortcoming of protontherapy is its inability to treat radioresistant cancers, being protons radiobiologically almost as effective as photons. Heavier particles, such as 12C ions, can overcome radioresistance but they present radiobiological and economic issues that hamper their widespread adoption. Therefore, many strategies have been designed to increase the biological effectiveness of proton beams. Examples are chemical radiosensitizing agents or, more recently, metallic nanoparticles. The goal of this project is to investigate the use of nuclear reactions triggered by protons generating short-range high- LET alpha particles inside the tumours, thereby allowing a highly localized DNA-damaging action. Specifically, we intend to consolidate and explain the promising results recently published in [2], where a significant enhancement of biological effectiveness was achieved by the p-11B reaction. Clinically relevant binary approaches were first proposed with Boron Neutron Capture Therapy (BNCT), which exploits thermal neutron capture in 10B, suitably accumulated into tumour before irradiation. The radiosensitising effects due to the presence of 10B will be compared to those elicited by p-11B, using the same carrier and relating the observed effects with intracellular 11B and 10B distribution as well as modelled particle action and measured dose deposition at the micro/nanometer scale. Moreover, the p-19F reaction, which also generates secondary particles potentially leading to local enhancement of proton effectiveness, will be investigated. The in-vivo imaging of 11B and 19F carriers will be studied, in particular by optimizing 19F-based magnetic resonance