Optimal low-thrust trajectories to asteroids through an algorithm based on differential dynamic programming

In this paper an optimisation algorithm based on Differential Dynamic Programming is applied to the design of rendezvous and fly-by trajectories to near Earth objects. Differential dynamic programming is a successive approximation technique that computes a feedback control law in correspondence of a fixed number of decision times. In this way the high dimensional problem characteristic of low-thrust optimisation is reduced into a series of small dimensional problems. The proposed method exploits the stage-wise approach to incorporate an adaptive refinement of the discretisation mesh within the optimisation process. A particular interpolation technique was used to preserve the feedback nature of the control law, thus improving robustness against some approximation errors introduced during the adaptation process. The algorithm implements global variations of the control law, which ensure a further increase in robustness. The results presented show how the proposed approach is capable of fully exploiting the multi-body dynamics of the problem; in fact, in one of the study cases, a fly-by of the Earth is scheduled, which was not included in the first guess solution

FREE SEARCH AND DIFFERENTIAL EVOLUTION TOWARDS DIMENSIONS NUMBER CHANGE

This paper presents an exploration of Free Search (FS) and modified Differential Evolution (DE) with enhanced adaptivity. The aim of the study is to identify how these methods can cope with changes of the number of variables of a hard design test, unaided. The results suggest that both methods can adapt successfully to the variation of the number of variables and constraint conditions. The results are presented. Contributions to the engineering design are replacement in high extent of human based search with machine based and movement of optimisation process from human guided to machine self guided search

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Adaptive intelligence applied to numerical optimisation

The article presents modification strategies theoretical comparison and experimental results achieved by adaptive heuristics applied to numerical optimisation of several non-constraint test functions. The aims of the study are to identify and compare how adaptive search heuristics behave within heterogeneous search space without retuning of the search parameters. The achieved results are summarised and analysed, which could be used for comparison to other methods and further investigation

The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

open access articleWe present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the Holm–Bonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them

Adaptive intelligence: essential aspects

The article discusses essential aspects of Adaptive Intelligence. Experimental results on optimisation of global test functions by Free Search, Differential Evolution, and Particle Swarm Optimisation clarify how these methods can adapt to multi-modal landscape and space dominated by sub-optimal regions, without supervisors’ control. The achieved results are compared and analysed

An Investigation Into the use of Swarm Intelligence for an Evolutionary Algorithm Optimisation; The Optimisation Performance of Differential Evolution Algorithm Coupled with Stochastic Diffusion Search

The integration of Swarm Intelligence (SI) algorithms and Evolutionary algorithms (EAs) might be one of the future approaches in the Evolutionary Computation (EC). This work narrates the early research on using Stochastic Diffusion Search (SDS) -- a swarm intelligence algorithm -- to empower the Differential Evolution (DE) -- an evolutionary algorithm -- over a set of optimisation problems. The results reported herein suggest that the powerful resource allocation mechanism deployed in SDS has the potential to improve the optimisation capability of the classical evolutionary algorithm used in this experiment. Different performance measures and statistical analyses were utilised to monitor the behaviour of the final coupled algorithm

Incremental solution of LTMGA transfers transcribed with an advanced shaping approach

In the last decade the global optimisation of low-thrust multi-gravity assist transfers (LTMGA) has been tackled with different approaches. Some authors proposed to generate a first guess solution by building a multi-gravity assist transfer with impulsive manoeuvres and then using a direct or an indirect method to transcribe the multi-impulse arcs into low-thrust arcs. Other authors, notably Petropoulos et al. (2002), De Pascale et al. (2006), Wall et al. (2008) and Schütze et al. (2009), proposed the use of several forms of trajectory shaping to model low-thrust arcs. The disadvantage in all these studies is that the swingbys are powered and therefore suggest the use of high thrust propulsion along with the low thrust propulsion on board the spacecraft. The problem generally resides in the lack of flexibility of the low thrust trajectory models to satisfy a variety of boundary conditions. In this paper, a spherical shaping model is used whereby all encountered types of boundary constraints are satisfied analytically. Furthermore, a special incremental pruning of the search space is performed before employing a global optimiser. The process is conceptually equivalent to the approach proposed by Becerra et al. for the search space pruning of multi-gravity assist trajectories and exploits the decoupling of pairs of transfer arcs. Such decoupling removes the dependency of one arc from all those that are two or more before, and allows for pruning the search space in polynomial time. Numerical examples are presented for LTMGA transfers from Earth to asteroid Apollo and Earth to Jupiter