A hybrid multi-objective optimization procedure using PCX based NSGA-II and sequential quadratic programming

Despite the existence of a number of procedures for multi-objective optimization using evolutionary algorithms, there is still the need for a systematic and unbiased comparison of different approaches on a carefully chosen set of test problems. In this paper, a hybrid approach using PCX based NSGA- II and sequential quadratic programming (SQP) is applied on 19 benchmark test problems consisting of two, three and five objectives. PCX-NSGA-II is used as a population based algorithm where SQP is used as a local search procedure. A population based approach helps in finding the non-dominated set of solutions with a good spread, whereas SQP improves the obtained set of non-dominated solutions locally. The results obtained by the present approach shows mixed performance on the chosen test problems

Multi agent collaborative search based on Tchebycheff decomposition

This paper presents a novel formulation of Multi Agent Collaborative Search, for multi-objective optimization, based on Tchebycheff decomposition. A population of agents combines heuristics that aim at exploring the search space both globally (social moves) and in a neighborhood of each agent (individualistic moves). In this novel formulation the selection process is based on a combination of Tchebycheff scalarization and Pareto dominance. Furthermore, while in the previous implementation, social actions were applied to the whole population of agents and individualistic actions only to an elite sub-population, in this novel formulation this mechanism is inverted. The novel agent-based algorithm is tested at first on a standard benchmark of difficult problems and then on two specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi objective optimization algorithms. The results demonstrate that this novel agent-based search has better performance with respect to its predecessor in a number of cases and converges better than the other state-of-the-art algorithms with a better spreading of the solutions

Multi-objective optimisation of low-thrust trajectories

This research work developed an innovative computational approach to the preliminary design of low-thrust trajectories optimising multiple mission criteria. Low-Thrust (LT) propulsion has become the propulsion system of choice for a number of near Earth and interplanetary missions. Consequently, in the last two decades a good wealth of research has been devoted to the development of computational method to design low-thrust trajectories. Most of the techniques, however, minimise or maximise a single figure of merit under a set of design constraints. Less effort has been devoted to the development of efficient methods for the minimisation (or maximisation) of two or more figures of merit. On the other hand, in the preliminary mission design phase, the decision maker is interested in analysing as many design solutions as possible against different trade-off criteria. Therefore, in this PhD work, an innovative Multi-Objective (MO), memetic optimisation algorithm, called Multi-Agent Collaborative Search (MACS2), has been implemented to tackle low-thrust trajectory design problems with multiple figures of merit. Tests on both academic and real-world problems showed that the proposed MACS2 paradigm performs better than or as well as other state-of-the-art Multi-Objective optimisation algorithms. Concurrently, a set of novel approximated, first-order, analytical formulae has been developed, to obtain a fast but reliable estimation of the main trade-off criteria. These formulae allow for a fast propagation of the orbital motion under a constant perturbing acceleration. These formulae have been shown to allow for the fast and relatively accurate propagation of long LT trajectories under the typical acceleration level delivered by current engine technology. Various applications are presented to demonstrate the validity of the combination of the analytical formulae with MACS2. Among them, the preliminary design of the JAXA low-cost DESTINY mission to L2, a novel approach to the optimisation under uncertainty of deflection actions for Near Earth Objects (NEO), and the de-orbiting of space debris with low-thrust and with a combination of low-thrust and solar radiation pressure