a b s t r a c tNew challenges in engineering design lead to multiobjective (multicriteria) problems. In this context, the Pareto front supplies a set of solutions where the designer (decision-maker) has to look for the best choice according to his preferences. Visualization techniques often play a key role in helping decision-makers, but they have important restrictions for more than two-dimensional Pareto fronts. In this work, a new graphical representation, called Level Diagrams, for n-dimensional Pareto front analysis is proposed. Level Diagrams consists of representing each objective and design parameter on separate diagrams. This new technique is based on two key points: classification of Pareto front points according to their proximity to ideal points measured with a specific norm of normalized objectives (several norms can be used); and synchronization of objective and parameter diagrams. Some of the new possibilities for analyzing Pareto fronts are shown. Additionally, in order to introduce designer preferences, Level Diagrams can be coloured, so establishing a visual representation of preferences that can help the decision-maker. Finally, an example of a robust control design is presented -a benchmark proposed at the American Control Conference. This design is set as a six-dimensional multiobjective problem.Ó 2008 Elsevier Inc. All rights reserved. MotivationIn numerous engineering areas, the task of obtaining suitable designs becomes a multiobjective (or multicriteria) problem. This means it is necessary to look for a solution in the design space that satisfies several specifications (objectives) in the performance space. Generally, these specifications are conflicting, that is, there is no simultaneous optimal solution for all of them. In this context, the solution is not unique, instead there is a set of possible solutions where none is best for all objectives. This set of optimal solutions in the design space is called the Pareto set. The region defined by the performances (the value of all objectives) for all Pareto set points is called the Pareto front.The exact determination of the Pareto front is unrealistic for real-world problems, as it is usually an infinite set. Therefore, it is usual to focus on obtaining a discrete approximation. A common step for solving a multiobjective optimization problem is to obtain the discrete approximation of the Pareto front. This is an open research field where numerous techniques have already been developed [19] and where new techniques are being constantly developed [17,14]. An alternative, and very active research line, is Multiobjective Evolutionary Algorithms [5,9]. In general, these algorithms supply reasonable solutions for Pareto front approximations. Once obtained, the next step for the designer is to select one, or more, solutions inside the Pareto front approximation. The final solution is often selected using methodologies that normally include designer 0020-0255/$ -see front matter Ó
SYNOPSIS Spontaneous extradural haemorrhage may be due to neighbourhood infections, vascular malformations of the dura mater, and disorders of blood coagulation. Two cases are described here: in one, infection was present; in the other, there was a berry aneurysm of the middle meningeal artery with a small parietal dural angioma. Operation was successful in both patients.Traumatic extradural haemorrhage occurs in
Thyroid cancer is the single most prevalent endocrine malignancy; differentiated thyroid cancer (DTC) accounts for more than 90 % of all malignancies and its incidence has been rising steadily. For more patients, surgical treatment, radioactive iodine (RAI) ablation, and thyroid-stimulating hormone (TSH) suppressive therapy achieve an overall survival (OS) rate of 97.7 % at 5 years. Nevertheless, locoregional recurrence occurs in up to 20 % and distant metastases in approximately 10 % at 10 years. Two-thirds of these patients will never be cured with radioactive iodine therapy and will become RAI-refractory, with a 3-year OS rate of less than 50 %. Over the last decade, substantial progress has been made in the management of RAI-refractory DTC. Given the controversy in some areas, the Spanish Task Force for Thyroid Cancer on behalf of Spanish Society of Endocrinology Thyroid Cancer Working Group (GTSEEN) and the Spanish Rare Cancer Working Group (GETHI) have created a national joint task force to reach a consensus addressing the most challenging aspects of management in these patients. In this way, multidisciplinary management should be mandatory and nuclear medicine targeted therapy, novel molecular targeted agents, and combinations are currently changing the natural history of RAI-refractory DTC.
In this paper, an optimal gain tuning method for PID controllers is proposed using a novel combination of a simplified Ant Colony Optimization algorithm and Nelder-Mead method (ACO-NM) including a new procedure to constrain NM. To address Proportional-Integral-Derivative (PID) controller tuning for the Automatic Voltage Regulator (AVR) system, this paper presents a metaanalysis of the literature on PID parameter sets solving the AVR problem. The investigation confirms that the proposed ACO-NM obtains better or equivalent PID solutions and exhibits higher computational efficiency than previously published methods. The proposed ACO-NM application is extended to realistic conditions by considering robustness to AVR process parameters, control signal saturation and noisy measurements as well as tuning a two-degree-of-freedom PID controller (2DOF-PID). For this type of PID, a new objective function is also proposed to manage control signal constraints. Finally, real time control experiments confirm the performance of the proposed 2DOF-PIDs in quasi-real conditions. Furthermore, the efficiency of the algorithm is confirmed by comparing its results to other optimization algorithms and NM combinations using benchmark functions.
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