Evaluation of a proper controller performance for maximum-power point tracking of a stand-alone PV system

In this paper the implementation of a suggested stand-alone PV system, for maximum-power point tracking (MPPT), is carried out. Also, this paper presents a comparative study, through experimental work, between the conventional PI controller and the fuzzy logic controller (FLC) under different atmospheric conditions. The implemented system with both the PI controller and the FLC gives a good maximum-power operation of the PV array, but the tracking capability for different optimum operating points is better and faster for the case of using the FLC compared to the case of using the PI controller.     

On an evolutionary approach for constrained optimization problem solving

Over the last few decades, many different evolutionary algorithms have been introduced for solving constrained optimization problems. However, due to the variability of problem characteristics, no single algorithm performs consistently over a range of problems. In this paper, instead of introducing another such algorithm, we propose an evolutionary framework that utilizes existing knowledge to make logical changes for better performance. The algorithmic aspects considered here are: the way of using search operators, dealing with feasibility, setting parameters, and refining solutions. The combined impact of such modifications is significant as has been shown by solving two sets of test problems: (i) a set of 24 test problems that were used for the CEC2006 constrained optimization competition and (ii) a second set of 36 test instances introduced for the CEC2010 constrained optimization competition. The results demonstrate that the proposed algorithm shows better performance in comparison to the state-of-the-art algorithms.     

Interleaving Guidance in Evolutionary Multi-Objective Optimization

In this paper, we propose a framework that uses localization for multi-objective optimization to simultaneously guide an evolutionary algorithm in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres （local models） in the decision space. These spheres axe usually guided, using some direction information, in the decision space towards the areas with non-dominated solutions. We use a second mechanism to adjust the spheres to specialize on different parts of the Paxeto front by using a guided dominance technique in the objective space. Through this interleaved guidance in both spaces, the spheres will be guided towards different parts of the Paxeto front while also exploring the decision space efficiently. The experimental results showed good performance for the local models using this dual guidance, in comparison with their original version.     

Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data

Neural Computing and Applications - Feature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model...     

A new iris localization method based on the competitive chords

This study introduces a novel iris localization method based on the competitive chords. The new method can be used to detect pupil?Ciris and iris?Csclera boundaries. The basic idea is to construct a set of chords from the left edges and the right edges of the pupil (or iris), and then find the winner chords with aligned centers. The winner chords can be used to vote to the correct pupil??s (or iris??s) center and radius. To verify the effectiveness of the proposed method, it is compared with two efficient techniques and applied to five datasets. The experimental results show that the new method is faster, more accurate and more robust than the state-of-the-art iris localization methods.     

Hybrid clustering analysis using improved krill herd algorithm

In this paper, a novel text clustering method, improved krill herd algorithm with a hybrid function, called MMKHA, is proposed as an efficient clustering way to obtain promising and precise results in this domain. Krill herd is a new swarm-based optimization algorithm that imitates the behavior of a group of live krill. The potential of this algorithm is high because it performs better than other optimization methods; it balances the process of exploration and exploitation by complementing the strength of local nearby searching and global wide-range searching. Text clustering is the process of grouping significant amounts of text documents into coherent clusters in which documents in the same cluster are relevant. For the purpose of the experiments, six versions are thoroughly investigated to determine the best version for solving the text clustering. Eight benchmark text datasets are used for the evaluation process available at the Laboratory of Computational Intelligence (LABIC). Seven evaluation measures are utilized to validate the proposed algorithms, namely, ASDC, accuracy, precision, recall, F-measure, purity, and entropy. The proposed algorithms are compared with the other successful algorithms published in the literature. The results proved that the proposed improved krill herd algorithm with hybrid function achieved almost all the best results for all datasets in comparison with the other comparative algorithms.     

Mapping concentrations of surface water quality parameters using a novel remote sensing and artificial intelligence framework

The deterioration of surface water quality occurs due to the presence of various types of pollutants generated from human, agricultural, and industrial activities. Thus, mapping concentrations of different surface water quality parameters (SWQPs), such as turbidity, total suspended solids (TSS), chemical oxygen demand (COD), biological oxygen demand (BOD), and dissolved oxygen (DO), is indeed critical for providing the appropriate treatment to the affected waterbodies. Traditionally, concentrations of SWQPs have been measured through intensive field work. Additionally, quite a lot of studies have attempted to retrieve concentrations of SWQPs from satellite images using regression-based methods. However, the relationship between SWQPs and satellite data is complex to be modelled accurately by using regression-based methods. Therefore, our study attempts to develop an artificial intelligence modelling method for mapping concentrations of both optical and non-optical SWQPs. In this context, a remote-sensing framework based on the back-propagation neural network (BPNN) is developed for the first time to quantify concentrations of different SWQPs from the Landsat8 satellite imagery. Compared to other methods, such as Support Vector Machine, significant coefficients of determination (R²) between the Landsat8 surface reflectance and concentrations of SWQPs were obtained using the developed Landsat8-based-BPNN models. The resulting R² values were 0.991, 0.933, 0.937, 0.930, and 0.934 for turbidity, TSS, COD, BOD, and DO, respectively. Indeed, these findings indicate that the developed Landsat8-based-BPNN framework is capable of developing highly accurate models for retrieving concentrations of different SWQPs from the Landsat8 imagery.     

On the Electromagnetic Interference Fringes Method in Geophysical Prospecting Applications

The method of electromagnetic interference fringes for the prospecting of conductive subsurfaces inside the earth is reviewed. The fields radiated by an electrically small horizontal loop situated on the earth's surface are derived and expressed in terms of different interfering wave components. The variable distance interference fringes (VDIF) method and the variable frequency interference (VFIF) method of prospecting are considered, and an interpretation procedure using the data obtained from both methods is suggested.