Hybrid non-dominated sorting genetic algorithm with adaptive operators selection

Multiobjective optimization entails minimizing or maximizing multiple objective functions subject to a set of constraints. Many real world applications can be formulated as multi-objective optimization problems (MOPs), which often involve multiple conflicting objectives to be optimized simultaneously. Recently, a number of multi-objective evolutionary algorithms (MOEAs) were developed suggested for these MOPs as they do not require problem specific information. They find a set of non-dominated solutions in a single run. The evolutionary process on which they are based, typically relies on a single genetic operator. Here, we suggest an algorithm which uses a basket of search operators. This is because it is never easy to choose the most suitable operator for a given problem. The novel hybrid non-dominated sorting genetic algorithm (HNSGA) introduced here in this paper and tested on the ZDT (Zitzler-Deb-Thiele) and CEC’09 (2009 IEEE Conference on Evolutionary Computations) benchmark problems specifically formulated for MOEAs. Numerical results prove that the proposed algorithm is competitive with state-of-the-art MOEAs

Hybrid adaptive evolutionary algorithm based on decomposition

The performance of search operators varies across the different stages of the search/optimization process of evolutionary algorithms (EAs). In general, a single search operator may not do well in all these stages when dealing with different optimization and search problems. To mitigate this, adaptive search operator schemes have been introduced. The idea is that when a search operator hits a difficult patch (under-performs) in the search space, the EA scheme “reacts” to that by potentially calling upon a different search operator. Hence, several multiple-search operator schemes have been proposed and employed within EA. In this paper, a hybrid adaptive evolutionary algorithm based on decomposition (HAEA/D) that employs four different crossover operators is suggested. Its performance has been evaluated on the well-known IEEE CEC’09 test instances. HAEA/D has generated promising results which compare well against several well-known algorithms including MOEA/D, on a number of metrics such as the inverted generational distance (IGD), the hyper-volume, the Gamma and Delta functions. These results are included and discussed in this paper

A comparative study on optimization methods for the constrained nonlinear programming problems

Constrained nonlinear programming problems often arise in many engineering applications. The most well-known optimization methods for solving these problems are sequential quadratic programming methods and generalized reduced gradient methods. This study compares the performance of these methods with the genetic algorithms which gained popularity in recent years due to advantages in speed and robustness. We present a comparative study that is performed on fifteen test problems selected from the literature

Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naive Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images

Reducing Design Risk Using Robust Design Methods: A Dual Response Surface Approach

Space transportation system conceptual design is a multidisciplinary process containing considerable element of risk. Risk here is defined as the variability in the estimated (output) performance characteristic of interest resulting from the uncertainties in the values of several disciplinary design and/or operational parameters. Uncertainties from one discipline (and/or subsystem) may propagate to another, through linking parameters and the final system output may have a significant accumulation of risk. This variability can result in significant deviations from the expected performance. Therefore, an estimate of variability (which is called design risk in this study) together with the expected performance characteristic value (e.g. mean empty weight) is necessary for multidisciplinary optimization for a robust design. Robust design in this study is defined as a solution that minimizes variability subject to a constraint on mean performance characteristics. Even though multidisciplinary design optimization has gained wide attention and applications, the treatment of uncertainties to quantify and analyze design risk has received little attention. This research effort explores the dual response surface approach to quantify variability (risk) in critical performance characteristics (such as weight) during conceptual design

Time Scale In Least Square Method

Study of dynamic equations in time scale is a new area in mathematics. Time scale tries to build a bridge between real numbers and integers. Two derivatives in time scale have been introduced and called as delta and nabla derivative. Delta derivative concept is defined as forward direction, and nabla derivative concept is defined as backward direction. Within the scope of this study, we consider the method of obtaining parameters of regression equation of integer values through time scale. Therefore, we implemented least squares method according to derivative definition of time scale and obtained coefficients related to the model. Here, there exist two coefficients originating from forward and backward jump operators relevant to the same model, which are different from each other. Occurrence of such a situation is equal to total number of values of vertical deviation between regression equations and observation values of forward and backward jump operators divided by two. We also estimated coefficients for the model using ordinary least squares method. As a result, we made an introduction to least squares method on time scale. We think that time scale theory would be a new vision in least square especially when assumptions of linear regression are violated.WoSScopu

Solving Large Scale Systems Of Linear Equations With A Stabilized Lanczos-Type Algorithms Running On A Cloud Computing Platform

The Lanczos-type algorithms for Systems of Linear Equations (SLEs) are efficient but fragile. A number of ways to resolve this issue have been suggested. But, the problem is still not fully sorted, in our view. Here, we suggest a way that takes advantage of the sequence of approximate solutions that have been computed prior to breakdown by embedding interpolation/extrapolation to avoid it. The approach, referred to as Embedded Interpolation-Extrapolation Model in Lanczos-type Algorithm (EIEMLA), generates new iterates which are at least as good as the best in the current sequence. This process is repeated after appending the new iterates to the sequence of approximate solutions until some convergence tolerance is achieved. To improve EIEMLA's convergence and stability, a restart version of REIEMLA is also considered. These algorithms are more robust than other Lanczos-type algorithms, including those with restarting and switching strategies. Both algorithms have been implemented to run in parallel on a Cloud computing platform. Our tests involve SLEs with up to 10(6) variables and equations. The results show that breakdown is mitigated and efficiency gains can be achieved through parallelization.WoSScopu

Hybrid Constrained Evolutionary Algorithm For Numerical Optimization Problems

Constrained optimization are naturally arises in many real-life applications, and is therefore gaining a constantly growing attention of the researchers. Evolutionary algorithms are not directly applied on constrained optimization problems. However, different constraint-handling techniques are incorporated in their framework to adopt it for dealing with constrained environments. This paper suggests an hybrid constrained evolutionary algorithm (HCEA) that employs two penalty functions simultaneously. The suggested HCEA has two versions namely HCEA-static and HCEA-adaptive. The performance of the HCEA-static and HCEA-adaptive algorithms are examined upon the constrained benchmark functions that are recently designed for the special session of the 2006 IEEE Conference of Evolutionary Computation (IEEE-CEC'06). The experimental results of the suggested algorithms are much promising as compared to one of the recent constrained version of the JADE. The converging behaviour of the both suggested algorithms on each benchmark function is encouraging and promising in most cases.WoSScopu

Abdominal cocoon syndrome as a rare cause of mechanical bowel obstruction: report of two cases

WOS: 000298313000015PubMed ID: 22290011An abdominal cocoon is an extremely rare condition, and has been reported mainly in young adolescent women as a cause of small bowel obstruction. In these patients, the small bowel is encased in a fibrous sac called an abdominal cocoon. We hereby present two cases who were diagnosed only by laparotomy and their correlation with the literature. They both received early intervention, thus preventing the need for bowel resection. The pathology of both membranes showed inflammation