A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimize the num- ber of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as Simulated Annealing and Variable Neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, Genetic Al- gorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a Controlled Migration operator into the GA heuristic, data, which repre- sents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the- loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits

A decision support methodology for process in the loop optimisation

Experimental optimisation with hardware-in-the-loop is a common procedure in engineering, particularly in cases where accurate modelling is not possible. A common methodology to support experimental search is to use one of the many gradient descent methods. However, even sophisticated and proven methodologies such as Simulated Annealing (SA) can be significantly challenged in the presence of significant noise. This paper introduces a decision support methodology based upon Response Surfaces (RS), which supplements experimental management based on variable neighbourhood search, and is shown to be highly effective in directing experiments in the presence of significant signal to noise (S-N) ratio and complex combinatorial functions. The methodology is developed on a 3-dimensional surface with multiple local-minima and large basin of attraction, and high S-N ratio. Finally, the method is applied to a real-life automotive experimental application

Estimation of Noisy Cost Functions by Conventional and Adjusted Simulated Annealing Techniques

L'algorithme de recuit simulé est largement utilisé dans la communauté d'optimisation pour résoudre divers types de problèmes, discrets et continus. L'objectif de cette thèse est d'analyser le recuit simulé dans des environnements déterministes et stochastiques pour des problèmes discrets. Les objectifs précis sont de classer des problèmes clés, d'offrir des suggestions et des recommandations à suivre en utilisant l'algorithme de recuit simulé et de recuit simulé sous bruit. Plus spécifiquement, des problèmes apparaissent en optimisation en présence de bruit, et sur la manière de le contrôler. Nous proposons la méthode de recuit simulé bruité (NSA: Noisy Simulated Annealing), basée sur la modification de l'algorithme de Metropolis-Hastings présentée par Ceperlay and Dewing, qui surpasse les techniques de recuit simulé analogues, délivrant des solutions numériques similaires, à coût réduit. Nous considérons les principales approches qui traitent le bruit dans le cadre du recuit simulé afin d'en extraire leurs attributs distinctifs et de produire une comparaison plus pertinente. Nous évaluons ensuite les performances numériques de l'approche sur des instances du problème du voyageur de commerce. Les résultats obtenus montrent un clair avantage pour le recuit simulé bruité, en présence de bruit.The Simulated Annealing (SA) algorithm is extensively used in the optimization community for solving various kinds of problems, discrete and continuous. This thesis aims to analyze SA in both deterministic and stochastic environments for discrete problems. Precise objectives are to classify key problems, offer suggestions and recommendations to be undertaken by using SA and Simulated Annealing Under Noise (SAUN). More specifically, problems appear in optimization due to the existence of noise when evaluating the objective function, and how to control this noise. We propose a method, called Noisy Simulated Annealing (NSA), based on the Metropolis-Hasting algorithm modification presented by Ceperlay and Dewing, that outperforms analogous SA techniques, delivering similar numerical solutions, at a reduced cost. We consider the main approaches in the SA setting that handle noise in order to extract their distinctive attributes and make the comparison more relevant. We next assess the numerical performance of the approach on traveling salesman problem instances. The outcomes of our tests show a clear advantage for NSA when solving different problems to get high-quality solutions in presence of noise

Detecting compact galactic binaries using a hybrid swarm-based algorithm

Compact binaries in our galaxy are expected to be one of the main sources of gravitational waves for the future eLISA mission. During the mission lifetime, many thousands of galactic binaries should be individually resolved. However, the identification of the sources, and the extraction of the signal parameters in a noisy environment are real challenges for data analysis. So far, stochastic searches have proven to be the most successful for this problem. In this work we present the first application of a swarm-based algorithm combining Particle Swarm Optimization and Differential Evolution. These algorithms have been shown to converge faster to global solutions on complicated likelihood surfaces than other stochastic methods. We first demonstrate the effectiveness of the algorithm for the case of a single binary in a 1 mHz search bandwidth. This interesting problem gave the algorithm plenty of opportunity to fail, as it can be easier to find a strong noise peak rather than the signal itself. After a successful detection of a fictitious low-frequency source, as well as the verification binary RXJ0806.3+1527, we then applied the algorithm to the detection of multiple binaries, over different search bandwidths, in the cases of low and mild source confusion. In all cases, we show that we can successfully identify the sources, and recover the true parameters within a 99\% credible interval.Comment: 19 pages, 5 figure