On the detection of nearly optimal solutions in the context of single-objective space mission design problems

When making decisions, having multiple options available for a possible realization of the same project can be advantageous. One way to increase the number of interesting choices is to consider, in addition to the optimal solution x*, also nearly optimal or approximate solutions; these alternative solutions differ from x* and can be in different regions – in the design space – but fulfil certain proximity to its function value f(x*). The scope of this article is the efficient computation and discretization of the set E of e–approximate solutions for scalar optimization problems. To accomplish this task, two strategies to archive and update the data of the search procedure will be suggested and investigated. To make emphasis on data storage efficiency, a way to manage significant and insignificant parameters is also presented. Further on, differential evolution will be used together with the new archivers for the computation of E. Finally, the behaviour of the archiver, as well as the efficiency of the resulting search procedure, will be demonstrated on some academic functions as well as on three models related to space mission design

Computing the set of Epsilon-efficient solutions in multiobjective space mission design

In this work, we consider multiobjective space mission design problems. We will start from the need, from a practical point of view, to consider in addition to the (Pareto) optimal solutions also nearly optimal ones. In fact, extending the set of solutions for a given mission to those nearly optimal significantly increases the number of options for the decision maker and gives a measure of the size of the launch windows corresponding to each optimal solution, i.e., a measure of its robustness. Whereas the possible loss of such approximate solutions compared to optimal—and possibly even ‘better’—ones is dispensable. For this, we will examine several typical problems in space trajectory design—a biimpulsive transfer from the Earth to the asteroid Apophis and two low-thrust multigravity assist transfers—and demonstrate the possible benefit of the novel approach. Further, we will present a multiobjective evolutionary algorithm which is designed for this purpose

Approximate solutions in space mission design

In this paper, we address multi-objective space mission design problems. From a practical point of view, it is often the case that,during the preliminary phase of the design of a space mission, the solutions that are actually considered are not 'optimal' (in the Pareto sense)but belong to the basin of attraction of optimal ones (i.e. they are nearly optimal). This choice is motivated either by additional requirements that the decision maker has to take into account or, more often, by robustness considerations. For this, we suggest a novel MOEA which is a modification of the well-known NSGA-II algorithm equipped with a recently proposed archiving strategy which aims at storing the set of approximate solutions of a given MOP. Using this algorithm we will examine some space trajectory design problems and demonstrate the benefit of the novel approach

Influence of the fiber direction on the tribological behavior of carbon-fiber reinforced epoxy composites manufactured by prepeg laminates

In this work, friction behavior of different carbon fiber-epoxy composites is evaluated. Linear friction tests are carried out and wear behaviour has been established from several parameters as wear depth, wear band width and macrostructural observation. The experimental variables considered were load (10-40N) and the fiber orientation with respect to the sliding direction (0°, 90° and multidirectional). Layers with the fiber oriented in the sliding direction showed larger wear due to cracks existing in the fibermatrix interface once the wear depth reached the fibers. The best results were obtained in those cases in which no influence of the resin matrix existed

An invitation to measure insect cold tolerance: Methods, approaches, and workflow.

Insect performance is limited by the temperature of the environment, and in temperate, polar, and alpine regions, the majority of insects must face the challenge of exposure to low temperatures. The physiological response to cold exposure shapes the ability of insects to survive and thrive in these environments, and can be measured, without great technical difficulty, for both basic and applied research. For example, understanding insect cold tolerance allows us to predict the establishment and spread of insect pests and biological control agents. Additionally, the discipline provides the tools for drawing physiological comparisons among groups in wider studies that may not be focused primarily on the ability of insects to survive the cold. Thus, the study of insect cold tolerance is of a broad interest, and several reviews have addressed the theories and advances in the field. Here, however, we aim to clarify and provide rationale for common practices used to study cold tolerance, as a guide for newcomers to the field, students, and those wishing to incorporate cold tolerance into a broader study. We cover the \u27tried and true\u27 measures of insect cold tolerance, the equipment necessary for these measurement, and summarize the ecological and biological significance of each. Finally, we suggest a framework and workflow for measuring cold tolerance and low temperature performance in insects

Surface plasmon excitation and manipulation in disordered two-dimensional nanoparticle arrays

We present experimental and numerical results of simultaneous surface plasmon polariton (SPP) excitation and in-plane manipulation with random arrays of gold nanoparticles. The recorded images were obtained by using leakage radiation microscopy (LRM) for the excitation wavelength of 633 nm and for di®erent densities of particles. The numerical model makes use of a composed analytic Green dyadic which takes into account near- and far-¯eld regions, with the latter being approximated by the part describing the scattering via excitation of SPP. The LRM optical images obtained are related to the calculated SPP intensity distributions demonstrating that the developed approach can be successfully used in studies of systems of closely spaced arrays

Metabolic profile in patients with newly diagnosed bipolar disorder and their unaffected first-degree relatives

Abstract Objective The prevalence of metabolic syndrome and insulin resistance is twice as high in patients with bipolar disorder compared with the general population, and possibly associated with a disabling illness trajectory of bipolar disorder, an increased risk of cardiovascular disease and premature death. Despite these detrimental effects, the prevalence of metabolic syndrome and insulin resistance in patients newly diagnosed with bipolar disorder and their unaffected first-degree relatives is largely unknown. Methods In a cross-sectional study of 206 patients with newly diagnosed bipolar disorder, 50 of their unaffected first-degree relatives and 109 healthy age- and sex-matched individuals, we compared the prevalence of metabolic syndrome and insulin resistance (HOMA-IR). In patients with bipolar disorder, we further investigated illness and medication variables associated with the metabolic syndrome and insulin resistance. Results Higher rates of metabolic syndrome (odds ratio = 3.529, 95% CI 1.378–9.041, P = 0.009) and levels of insulin resistance (B = 1.203, 95% CI 1.059–1.367, P = 0.005) were found in patients newly diagnosed with bipolar disorder, but not in their unaffected first-degree relatives compared with matched healthy individuals (data adjusted for sex and age). Most patients with bipolar disorder (94.7%) were diagnosed within the preceding 2 years, and the average illness duration, defined as time from first mood episode, was 10 years. Conclusion Our findings of elevated prevalence of metabolic syndrome and insulin resistance in patients with newly diagnosed bipolar disorder highlight the importance of screening for these conditions at an early stage to employ adequate and early care reducing the risk of cardiovascular disease and premature death

Optimising decision trees using multi-objective particle swarm optimisation

Copyright © 2009 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Swarm Intelligence for Multi-objective Problems in Data MiningSummary. Although conceptually quite simple, decision trees are still among the most popular classifiers applied to real-world problems. Their popularity is due to a number of factors – core among these is their ease of comprehension, robust performance and fast data processing capabilities. Additionally feature selection is implicit within the decision tree structure. This chapter introduces the basic ideas behind decision trees, focusing on decision trees which only consider a rule relating to a single feature at a node (therefore making recursive axis-parallel slices in feature space to form their classification boundaries). The use of particle swarm optimization (PSO) to train near optimal decision trees is discussed, and PSO is applied both in a single objective formulation (minimizing misclassification cost), and multi-objective formulation (trading off misclassification rates across classes). Empirical results are presented on popular classification data sets from the well-known UCI machine learning repository, and PSO is demonstrated as being fully capable of acting as an optimizer for trees on these problems. Results additionally support the argument that multi-objectification of a problem can improve uni-objective search in classification problems