Browsing by Author "Yibre, Abdulkerim Mohammed"

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Citation - WoS: 3
Citation - Scopus: 5
Improving Artificial Algae Algorithm Performance by Predicting Candidate Solution Quality
(PERGAMON-ELSEVIER SCIENCE LTD, 2020) Yibre, Abdulkerim Mohammed; Koçer, Barış
The success of optimization algorithms is most of the time directly proportional to the number of fitness evaluations. However, not all fitness evaluations lead to successful fitness updates. Besides, the maximum number of fitness evaluations is limited and also balance of exploration and exploitation is still challenging. Best possible solution should be found in a reasonable time. Surely it can be said more fitness evaluation takes more time. Since methods are tested under fixed numbers of maximum fitness evaluation and the duration of each fitness evaluation of a problem may vary depending on the characteristic of the problem, finding best result with fewer fitness evaluations is challenging in optimization algorithms. For that reason in this study, we proposed a new method that predicts the quality of a candidate solution before evaluation of its fitness employing Gaussian-based Naive Bayes probabilistic model. If the candidate solution is predicted to generate good result then that solution is evaluated by the objective function. Otherwise new candidate solution is created as usual. The primary purpose of the proposed method is improving the performance of AAA and at the same time preventing unnecessary fitness evaluation. The proposed method is evaluated using standard benchmark functions and CEC'05 test suite. The obtained results suggests that the new method outperformed the basic AAA and other state-of-the-art meta-heuristic algorithms with fewer fitness evaluations. Thus, the new method can be extended to cost sensitive industrial problems. (c) 2020 Elsevier Ltd. All rights reserved.
Citation - WoS: 24
Citation - Scopus: 33
Semen Quality Predictive Model Using Feed Forwarded Neural Network Trained by Learning-Based Artificial Algae Algorithm
(ELSEVIER - DIVISION REED ELSEVIER INDIA PVT LTD, 2021) Yibre, Abdulkerim Mohammed; Koçer, Barış
Recent scientific studies have noted that the seminal quality of males is significantly decreasing due to lifestyle and environmental factors. Clinical diagnosis of sperm quality is one important aspect of identifying the potential of semen for the occurrence of pregnancy. Due to the advances in machine learning algorithms, especially the reliable and high classification accuracy of neural network in health related problems, it is becoming possible to predict seminal quality from lifestyle data. In this respect, a few attempts were made in predicting seminal quality. These studies were conducted using imbalanced data sets, where the performance outcomes tend to be biased towards the majority class. Other studies implemented the gradient descent technique for training the neural network. The gradient descent is a local training technique that is prone to get stuck to local minima. On the contrary, meta-heuristic algorithms enable searching solutions both locally and globally. Therefore, in this study, Artificial Algae Algorithm that is improved using a Learning-Based fitness evaluation method is proposed for training Feed Forward Neural Network (FFNN). In addition, the SMOTE data balancing method was employed to balance normal and abnormal instances. Experimental analyses were carried out to evaluate the predictive accuracy of the FFNN trained using Learning-Based Artificial Algae Algorithm (FFNN-LBAAA). The results were compared with well-known machine learning algorithms, namely: Multi-layer Perceptron Neural Network (MLP), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) algorithms. The proposed approach showed superior performance in discriminating normal and abnormal semen quality instances over the other compared algorithms. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.
Citation - WoS: 3
Citation - Scopus: 5
Weight Optimization of Hybrid Composite Laminate Using Learning-Oriented Artificial Algae Algorithm
(SPRINGER INTERNATIONAL PUBLISHING AG, 2020) Yibre, Abdulkerim Mohammed; Koçer, Barış; Esleman, Esmael Adem; Önal, Gürol
The optimal design of parameters is vital for the effective use of hybrid composite laminated structures. This is due to a highly dependent property of laminated composite structures strength on its fiber orientation, stacking sequence and the number of ply in each laminate. The main aim of this study is to apply Learning-Oriented Artificial Algae Algorithm for optimization of the weight of rectangular hybrid composite laminated plate subjected to compressive in-plane loading. The design parameters are number of plies and stacking sequence of the laminate. The critical buckling factor is the constraint of the optimization process. The parameters of the hybrid composite plate are optimized using Learning-Oriented Artificial Algae Algorithm with the aim of minimizing weight.The performance of the algorithm was compared with previous studies that employed the GA and ACO algorithms. The Learning-Oriented method is integrated to reduce the number of functions evaluated and in turn reducing computational cost. The results showed that Learning-Oriented Artificial Algae Algorithm outperformed GA and ACO, and hence can be successfully applied in the optimization of laminated composite structures.