Double-shock control bump design optimization using hybridized evolutionary algorithms

This study investigates the application of two advanced optimization methods for solving active flow control (AFC) device shape design problem and compares their optimization efficiency in terms of computational cost and design quality. The first optimization method uses hierarchical asynchronous parallel multi-objective evolutionary algorithm and the second uses hybridized evolutionary algorithm with Nash-Game strategies (Hybrid-Game). Both optimization methods are based on a canonical evolution strategy and incorporate the concepts of parallel computing and asynchronous evaluation. One type of AFC device named shock control bump (SCB) is considered and applied to a natural laminar flow (NLF) aerofoil. The concept of SCB is used to decelerate supersonic flow on suction/pressure side of transonic aerofoil that leads to a delay of shock occurrence. Such active flow technique reduces total drag at transonic speeds which is of special interest to commercial aircraft. Numerical results show that the Hybrid-Game helps an EA to accelerate optimization process. From the practical point of view, applying a SCB on the suction and pressure sides significantly reduces transonic total drag and improves lift-to-drag (L/D) value when compared to the baseline design.

Closing the gap between bandit and full-information online optimization : high-probability regret bound

We demonstrate a modification of the algorithm of Dani et al for the online linear optimization problem in the bandit setting, which allows us to achieve an O( \sqrt{T ln T} ) regret bound in high probability against an adaptive adversary, as opposed to the in expectation result against an oblivious adversary of Dani et al. We obtain the same dependence on the dimension as that exhibited by Dani et al. The results of this paper rest firmly on those of Dani et al and the remarkable technique of Auer et al for obtaining high-probability bounds via optimistic estimates. This paper answers an open question: it eliminates the gap between the high-probability bounds obtained in the full-information vs bandit settings.

On the data consumption benefits of accepting increased uncertainty

In the context of learning paradigms of identification in the limit, we address the question: why is uncertainty sometimes desirable? We use mind change bounds on the output hypotheses as a measure of uncertainty and interpret ‘desirable’ as reduction in data memorization, also defined in terms of mind change bounds. The resulting model is closely related to iterative learning with bounded mind change complexity, but the dual use of mind change bounds — for hypotheses and for data — is a key distinctive feature of our approach. We show that situations exist where the more mind changes the learner is willing to accept, the less the amount of data it needs to remember in order to converge to the correct hypothesis. We also investigate relationships between our model and learning from good examples, set-driven, monotonic and strong-monotonic learners, as well as class-comprising versus class-preserving learnability.

Analysis of efficiency and optimization of plasmon energy coupling into nanofocusing metal wedges

In this paper, we investigate theoretically and numerically the efficiency of energy coupling from a plasmon generated by a grating coupler at one of the interfaces of a metal wedge into the plasmonic eigenmode (i.e., symmetric or quasisymmetric plasmon) experiencing nanofocusing in the wedge. Thus the energy efficiency of energy coupling into metallic nanofocusing structure is analyzed. Two different nanofocusing structures with the metal wedge surrounded by a uniform dielectric (symmetric structure) and with the metal wedge enclosed between a substrate and a cladding with different dielectricpermittivities (asymmetric structure) are considered by means of the geometrical optics (adiabatic) approximation. It is demonstrated that the efficiency of the energy coupling from the plasmon generated by the grating into the symmetric or quasisymmetric plasmon experiencing nanofocusing may vary between ∼50% to ∼100%. In particular, even a very small difference (of ∼1%–2%) between the permittivities of the substrate and the cladding may result in a significant increase in the efficiency of the energy coupling (from ∼50% up to ∼100%) into the plasmon experiencing nanofocusing. Distinct beat patterns produced by the interference of the symmetric (quasisymmetric) and antisymmetric (quasiantisymmetric) plasmons are predicted and analyzed with significant oscillations of the magnetic and electric field amplitudes at both the metal wedge interfaces. Physical interpretations of the predicted effects are based upon the behavior, dispersion, and dissipation of the symmetric (quasisymmetric) and antisymmetric (quasiantisymmetric) filmplasmons in the nanofocusing metal wedge. The obtained results will be important for optimizing metallic nanofocusing structures and minimizing coupling and dissipative losses.

An approach to access control under uncertainty

In dynamic and uncertain environments such as healthcare, where the needs of security and information availability are difficult to balance, an access control approach based on a static policy will be suboptimal regardless of how comprehensive it is. The uncertainty stems from the unpredictability of users’ operational needs as well as their private incentives to misuse permissions. In Role Based Access Control (RBAC), a user’s legitimate access request may be denied because its need has not been anticipated by the security administrator. Alternatively, even when the policy is correctly specified an authorised user may accidentally or intentionally misuse the granted permission. This paper introduces a novel approach to access control under uncertainty and presents it in the context of RBAC. By taking insights from the field of economics, in particular the insurance literature, we propose a formal model where the value of resources are explicitly defined and an RBAC policy (entailing those predictable access needs) is only used as a reference point to determine the price each user has to pay for access, as opposed to representing hard and fast rules that are always rigidly applied.

High-probability regret bounds for bandit online linear optimization

We present a modification of the algorithm of Dani et al. [8] for the online linear optimization problem in the bandit setting, which with high probability has regret at most O ∗ ( √ T) against an adaptive adversary. This improves on the previous algorithm [8] whose regret is bounded in expectation against an oblivious adversary. We obtain the same dependence on the dimension (n 3/2) as that exhibited by Dani et al. The results of this paper rest firmly on those of [8] and the remarkable technique of Auer et al. [2] for obtaining high probability bounds via optimistic estimates. This paper answers an open question: it eliminates the gap between the high-probability bounds obtained in the full-information vs bandit settings.

Stability and change

This special feature section of Journal of Management & Organization (Volume 17/1 - March 2011) sets out to widen understanding of the processes of stability and change in today's organizations, with a particular emphasis on the contribution of institutional approaches to organizational studies. Institutional perspectives on organization theory assume that rational, economic calculations, such as the maximization of profits or the optimization of resource allocation, are not sufficient to understand the behavior of organizations and their strategic choices. Institutionalists acknowledge the great uncertainty associated with the conduct of organizations and suggest that taken-for-granted values, beliefs and meanings within and outside organizations also play an important role in the determination of legitimate action.

Intolerance of uncertainty and meta-worry : relative importance in discriminating GAD

This study explored whether intolerance of uncertainty and/or meta-worry discriminate between non-clinical individuals and those diagnosed with generalised anxiety disorder (GAD group). The participants were 107 GAD clients and 91 university students. The students were divided into two groups (high and low GAD symptom groups). A multivariate analysis of covariance (MANCOVA) adjusting for age indicated that intolerance of uncertainty distinguished between the low GAD symptom group and the high GAD symptom group, and between the low GAD symptom group and the GAD group. Meta-worry distinguished all three groups. A discriminant function including intolerance of uncertainty and meta-worry classified 94.4% of the GAD group and 97.9% of the low GAD symptom group. Only 6.8% of the high GAD symptom group was classified correctly, 77.3% of the high GAD symptom group was classified as GAD. Findings indicated that intolerance of uncertainty and meta-worry may assist with the diagnosis and treatment of GAD.

The relationship of meta-worry and intolerance of uncertainty with pathological worry, anxiety, and depression

This study explored how meta-worry and intolerance of uncertainty relate to pathological worry, generalised anxiety, obsessive compulsive disorder, social phobia, and depression. University students (n = 253) completed a questionnaire battery. A series of regression analyses were conducted. The results indicated that meta-worry was associated with GAD, social phobia, obsessive compulsive, and depressive symptoms. Intolerance of uncertainty was related to GAD, social phobia, and obsessive compulsive symptoms, but not depressive symptoms. The importance of meta-worry and intolerance of uncertainty as predictors of pathological worry, GAD, social phobia, obsessive compulsive and depressive symptoms was also examined. Even though both factors significantly predicted the aforementioned symptoms, meta-worry emerged as a stronger predictor of GAD and obsessive compulsive symptoms than did intolerance of uncertainty. Intolerance of uncertainty, compared with meta-worry, appeared as a stronger predictor of social phobia symptoms. Findings emphasise the importance of addressing meta-worry and/or intolerance of uncertainty not only for the assessment and treatment of generalised anxiety disorder (GAD), but also obsessive compulsive disorder, social phobia, and depression.

Efficient hybrid-game strategies coupled to evolutionary algorithms for robust multidisciplinary design optimization in aerospace engineering

A number of game strategies have been developed in past decades and used in the fields of economics, engineering, computer science, and biology due to their efficiency in solving design optimization problems. In addition, research in multiobjective and multidisciplinary design optimization has focused on developing a robust and efficient optimization method so it can produce a set of high quality solutions with less computational time. In this paper, two optimization techniques are considered; the first optimization method uses multifidelity hierarchical Pareto-optimality. The second optimization method uses the combination of game strategies Nash-equilibrium and Pareto-optimality. This paper shows how game strategies can be coupled to multiobjective evolutionary algorithms and robust design techniques to produce a set of high quality solutions. Numerical results obtained from both optimization methods are compared in terms of computational expense and model quality. The benefits of using Hybrid and non-Hybrid-Game strategies are demonstrated.