A Chaotic Approach of Differential Evolution Optimization Applied to Loudspeaker Design Problem

Over the past few years, the field of global optimization has been very active, producing different kinds of deterministic and stochastic algorithms for optimization in the continuous domain. These days, the use of evolutionary algorithms (EAs) to solve optimization problems is a common practice due to their competitive performance on complex search spaces. EAs are well known for their ability to deal with nonlinear and complex optimization problems. Differential evolution (DE) algorithms are a family of evolutionary optimization techniques that use a rather greedy and less stochastic approach to problem solving, when compared to classical evolutionary algorithms. The main idea is to construct, at each generation, for each element of the population a mutant vector, which is constructed through a specific mutation operation based on adding differences between randomly selected elements of the population to another element. Due to its simple implementation, minimum mathematical processing and good optimization capability, DE has attracted attention. This paper proposes a new approach to solve electromagnetic design problems that combines the DE algorithm with a generator of chaos sequences. This approach is tested on the design of a loudspeaker model with 17 degrees of freedom, for showing its applicability to electromagnetic problems. The results show that the DE algorithm with chaotic sequences presents better, or at least similar, results when compared to the standard DE algorithm and other evolutionary algorithms available in the literature.

A new approach for modeling and control of nonlinear systems via norm-bounded linear differential inclusions

A systematic approach to model nonlinear systems using norm-bounded linear differential inclusions (NLDIs) is proposed in this paper. The resulting NLDI model is suitable for the application of linear control design techniques and, therefore, it is possible to fulfill certain specifications for the underlying nonlinear system, within an operating region of interest in the state-space, using a linear controller designed for this NLDI model. Hence, a procedure to design a dynamic output feedback controller for the NLDI model is also proposed in this paper. One of the main contributions of the proposed modeling and control approach is the use of the mean-value theorem to represent the nonlinear system by a linear parameter-varying model, which is then mapped into a polytopic linear differential inclusion (PLDI) within the region of interest. To avoid the combinatorial problem that is inherent of polytopic models for medium- and large-sized systems, the PLDI is transformed into an NLDI, and the whole process is carried out ensuring that all trajectories of the underlying nonlinear system are also trajectories of the resulting NLDI within the operating region of interest. Furthermore, it is also possible to choose a particular structure for the NLDI parameters to reduce the conservatism in the representation of the nonlinear system by the NLDI model, and this feature is also one important contribution of this paper. Once the NLDI representation of the nonlinear system is obtained, the paper proposes the application of a linear control design method to this representation. The design is based on quadratic Lyapunov functions and formulated as search problem over a set of bilinear matrix inequalities (BMIs), which is solved using a two-step separation procedure that maps the BMIs into a set of corresponding linear matrix inequalities. Two numerical examples are given to demonstrate the effectiveness of the proposed approach.

New approach for power transformer protection based on intelligent hybrid systems

A power transformer needs continuous monitoring and fast protection as it is a very expensive piece of equipment and an essential element in an electrical power system. The most common protection technique used is the percentage differential logic, which provides discrimination between an internal fault and different operating conditions. Unfortunately, there are some operating conditions of power transformers that can mislead the conventional protection affecting the power system stability negatively. This study proposes the development of a new algorithm to improve the protection performance by using fuzzy logic, artificial neural networks and genetic algorithms. An electrical power system was modelled using Alternative Transients Program software to obtain the operational conditions and fault situations needed to test the algorithm developed, as well as a commercial differential relay. Results show improved reliability, as well as a fast response of the proposed technique when compared with conventional ones.

Plasma cortisol in first episode drug-naive mania: Differential levels in euphoric versus irritable mood

Background: Dysregulation of HPA axis has been widely described in subjects with bipolar disorder (BD), including changes in cortisol levels during mood episodes and euthymia. However, most of the studies were done with medicated BD patients with variable length of illness, which was shown to interfere on peripheral cortisol levels. Therefore, the present study aims to evaluate plasma cortisol levels in drug-naive BD subjects during the first manic episode, as well as investigate the relationship between plasma cortisol levels and manic symptomatology. Methods: Twenty-six drug-naive patients were enrolled meeting criteria for a first manic episode in bipolar I disorder. Severity of mania was assessed using the Young Mania Rating Scale (YMRS). The control group included 27 healthy subjects matched by age and gender. Cortisol was quantified using a direct radioimmunoassay. Results: Plasma cortisol levels were decreased during first manic episode compared to healthy controls. Higher cortisol levels were positively associated with the presence of irritability (dysphoria), while elated mania showed lower cortisol levels compared to controls. Limitation: Data including larger samples are lacking. Conclusion: Higher cortisol in dysphoric mania compared to predominantly elated/euphoric mania may indicate a clinical and neurobiological polymorphic phenomenon, potentially involving a higher biological sensitivity to stress in the presence of irritable mood. The present findings highlight the importance to add a dimensional approach to the traditional categorical diagnosis for future neurobiological studies in BD. (C) 2011 Elsevier B.V. All rights reserved.

Simplified approach to low-cost multiparameter monitoring based on low frequency polarization modulation

We experimentally revisit a technique of low-cost multiparameter monitor for optical performance monitoring based on low frequency polarization modulation. A simplified calibration procedure, which significantly reduces the mathematical complexity and processing effort is proposed. Validation is achieved by carrying out relative optical power, wavelength, and differential group delay measurements. (C) 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:18201824, 2012; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26956

Investigating Smart Sampling as a population initialization method for Differential Evolution in continuous problems

Recently, researches have shown that the performance of metaheuristics can be affected by population initialization. Opposition-based Differential Evolution (ODE), Quasi-Oppositional Differential Evolution (QODE), and Uniform-Quasi-Opposition Differential Evolution (UQODE) are three state-of-the-art methods that improve the performance of the Differential Evolution algorithm based on population initialization and different search strategies. In a different approach to achieve similar results, this paper presents a technique to discover promising regions in a continuous search-space of an optimization problem. Using machine-learning techniques, the algorithm named Smart Sampling (SS) finds regions with high possibility of containing a global optimum. Next, a metaheuristic can be initialized inside each region to find that optimum. SS and DE were combined (originating the SSDE algorithm) to evaluate our approach, and experiments were conducted in the same set of benchmark functions used by ODE, QODE and UQODE authors. Results have shown that the total number of function evaluations required by DE to reach the global optimum can be significantly reduced and that the success rate improves if SS is employed first. Such results are also in consonance with results from the literature, stating the importance of an adequate starting population. Moreover, SS presents better efficacy to find initial populations of superior quality when compared to the other three algorithms that employ oppositional learning. Finally and most important, the SS performance in finding promising regions is independent of the employed metaheuristic with which SS is combined, making SS suitable to improve the performance of a large variety of optimization techniques. (C) 2012 Elsevier Inc. All rights reserved.

Multiobjective Biogeography-Based Optimization Based on Predator-Prey Approach

Biogeography is the science that studies the geographical distribution and the migration of species in an ecosystem. Biogeography-based optimization (BBO) is a recently developed global optimization algorithm as a generalization of biogeography to evolutionary algorithm and has shown its ability to solve complex optimization problems. BBO employs a migration operator to share information between the problem solutions. The problem solutions are identified as habitat, and the sharing of features is called migration. In this paper, a multiobjective BBO, combined with a predator-prey (PPBBO) approach, is proposed and validated in the constrained design of a brushless dc wheel motor. The results demonstrated that the proposed PPBBO approach converged to promising solutions in terms of quality and dominance when compared with the classical BBO in a multiobjective version.

A simple-versatile approach to achieve all-Si-based optical micro-cavities

At present, solid thin films are recognized by their well established and mature processing technology that is able to produce components which, depending on their main characteristics, can perform either passive or active functions. Additionally, Si-based materials in the form of thin films perfectly match the concept of miniaturized and low-consumption devices-as required in various modern technological applications. Part of these aspects was considered in the present work that was concerned with the study of optical micro-cavities entirely based on silicon and silicon nitride thin films. The structures were prepared by the sputtering deposition method which, due to the adopted conditions (atmosphere and deposition rate) and arrangement of layers, provided cavities operating either in the visible (at ~ 670 nm) or in the near-infrared (at ~ 1560 nm) wavelength ranges. The main differential of the work relies on the construction of optical microcavities with a reduced number of periods whose main properties can be changed by thermal annealing treatments. The work also discusses the angle-dependent behavior of the optical transmission profiles as well as the use of the COMSOL software package to simulate the microcavities.