In this paper, we present a two phase local global search algorithm that is used to remedy the problems associated to the presence of sensitive local optima. However, The presence of such optima in most optimization problems make the global optimization very difficult in the sense that, as soon as the design space exhibits such local optima, the optimization method falls inside and are unable to leave it to a potentially better region. To accurate this problem we propose a new global search technique, which is called Circular Design. We propose also a new point scattering design and a new population evolution scheme the new algorithm works on the principal of evaluating a set of super individuals only. The local search is invoked at each time where a reallocation of the center of the Circular Design is needed, and it has the ability of significantly enlarge the attraction basin of the global optimum in order to reduce the probability of a possible convergence to an interesting local optimum. To illustrate the effectiveness of the proposed algorithm, numerical applications are performed with different benchmark problems; and the obtained results are satisfactory in terms of the solution quality and the time need to reach the global optimum.

The purpose of this work is to develop a computer‐aided diagnosis (CAD) system to assist radiologists in the classification of mammogram images. The CAD system is composed of three main steps. The first step is image preprocessing and segmentation with the seeded region growing algorithm applied on a localized triangular region to remove only the muscle. In the second step of the CAD system, we proposed a novel features extraction method, which consists of three stages. In the first, the discrete cosine transform (DCT) is applied on all obtained regions of interest and then only the upper left corner (ULC) of DCT coefficients is retained. Second, we have applied the energy probability to the ULCs that is used as a criterion for selecting discriminant information. At the last stage, a new Most Discriminative power coefficient algorithm has been proposed to select the most significant features. In the final step of the CAD, the support vector machines, Naive Bayes, and artificial neural network (ANN) classifiers are used to make an effective classification. The evaluation of the proposed algorithm on the mini‐Mammographic Image Analysis Society database shows its efficiency over other recently proposed CAD systems in the literature, whereas an accuracy of 100% can be achieved using ANN with a small number of features.

In this paper, an improvement of the particle swarm optimization algorithm is proposed. The aim of this algorithm is to determine the optimal parameters of a robust fractional order controller which guarantees stability and robustness of required nominal performances. Controllers with fractional order are first obtained by a previous transformation of the multi-objective optimization problem into an equivalent single-optimization problem, and then solved by the proposed improved particle swarm optimization algorithm. In order to examine the stability and performance robustness, this controller is applied on an ill-conditioned wind turbine equipped with a doubly fed asynchronous machine, where the system dynamics is modeled by an unstructured output multiplicative uncertainty model. The simulation results show the effectiveness of the proposed synthesis method, where the control is compared (for the same design frequency-domain specifications) for both the robust fractional order controller such as that designed through the standard particle swarm optimization algorithm and that obtained by resolving the multi-objective optimization problem.

No abstract

No abstract