Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
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Article Citation - WoS: 5Citation - Scopus: 4Uv-Visible Spectrophotometric Quantitative Analysis of Ternary Mixture Using Multivariate Calibration Methods Optimized by a Genetic Algorithm(Chiminform Data S A, 2010) Ozdemir, Durmus; Baleanu, Dumitru; Dinc, Erdal; Baleanu, Dumitru; MatematikSimultaneous determination of ternary mixtures of caffeine, paracetamol and metamizol in commercial tablet formulations using UV-visible spectro photometry combined with classical least squares (CLS) and genetic algorithm (GA) based multivariate calibration methods were demonstrated. The three genetic multivariate calibration methods are named as Genetic Classical Least Squares (GCLS), Genetic Inverse Least Squares (GILS) and Genetic Regression (GR). The GR method is based on a genetic algorithm based wavelength selection followed by a simple linear regression step whereas the GCLS and GILS are multivariate calibration methods modified by a wavelength selection principle using a genetic algorithm. The sample data set contains the UV-visible spectra of 47 synthetic mixtures (4 to 48 mu g/mL) and 16 tablets containing these components from two different producers. The spectra cover the range from 200 to 330 nm in 0.1 nm intervals. Several calibration models were built with the four methods for the three components. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) for the synthetic data were in the range of 0.04 and 2.34 mu g/mL for all the four methods. Predictive ability of the calibration models generated with synthetic samples was tested with actual tablet samples and results obtained from four methods were compared. The SEP values for the tablets were in the range of 0.31 and 15.44 mg/tablets.Conference Object Strategy Creation, Decomposition and Distribution in Particle Navigation: Memory Module(IFAC Secretariat, 2005) Beldek, U.; Leblebicioglu, K.in particle navigation problem strategy development is crucial. The difficulties encountered by the particles during their navigation tasks require different approaches in problem solving. One way to overcome the difficulties is to divide the problem into simple modules and develop solutions for these modules separately. Basically, two different modules are sufficient in addition to the main body to develop a successful solver. The first module (conflict module), which is developed by genetic programming, is used when the particles are in conflict. The second module (memory module) helps the particles to escape from local regions. Copyright © 2005 IFAC.Article Citation - WoS: 5Citation - Scopus: 9Strategy Creation, Decomposition and Distribution in Particle Navigation(Elsevier Science inc, 2007) Leblebicioglu, Kemal; Beldek, UlasStrategy planning is crucial to control a group to achieve a number of tasks in a closed area full of obstacles. In this study, genetic programming has been used to evolve rule-based hierarchical structures to move the particles in a grid region to accomplish navigation tasks. Communications operations such as receiving and sending commands between particles are also provided to develop improved strategies. In order to produce more capable strategies, a task decomposition procedure is proposed. In addition, a conflict module is constructed to handle the challenging situations and conflicts such as blockage of a particle's pathway to destination by other particles. (C) 2006 Elsevier Inc. All rights reserved.Article Citation - WoS: 12Citation - Scopus: 17Dynamics of Multi-Point Singular Fifth-Order Lane-Emden System With Neuro-Evolution Heuristics(Springer Heidelberg, 2022) Ali, Mohamed R.; Fathurrochman, Irwan; Raja, Muhammad Asif Zahoor; Sadat, R.; Baleanu, Dumitru; Sabir, ZulqurnainThe objective of the presented communication is to examine and analyze the solutions of nonlinear multi-singular fifth-order Lane-Emden (LE) system for different scenarios by variation of shape factors settled on the equivalent design of the LE equations. The neuro-evolution based stochastic computing is explored for the numerical measures using the artificial neural networks (ANNs) models for the appropriate continuous mapping, while the learning of decision variables is conducted using the integrated meta-heuristic global search of genetic algorithms (GA) hybrid with the local search efficiency of active-set (AS) i.e., ANN-GA-AS scheme. The numerical approach ANN-GA-AS is applied efficiently for the fifth kind of nonlinear LE model and statistical calculations further validate the accuracy, robustness as well as convergence.Article Citation - Scopus: 7Dynamics of Three-Point Boundary Value Problems With Gudermannian Neural Networks(Springer Science and Business Media Deutschland GmbH, 2023) Raja, M.A.Z.; Sadat, R.; Baleanu, D.; Sabir, Z.; Ali, M.R.The present study articulates a novel heuristic computing design with artificial intelligence algorithm by manipulating the models with Feed forward (FF) Gudermannian neural networks (GNN) accomplished with global search capability of Genetic algorithms (GA) combined with rapid local convergence of Active-set method (ASM), i.e., FF-GNN-GAASM for solving the second kind of Three-point singular boundary value problems (TPS-BVPs). The proposed FF-GNN-GAASM intelligent computing solver integrated into the hidden layer structure of FF-GNN systems of differential operatives of the second kind of STP-BVPs, which are linked to form the error based Merit function (MF). The MF is optimized with the hybrid-combined heuristics of GAASM. The stimulation for presenting this research work comes from the objective to introduce a reliable framework that associates the operational features of NNs to challenge with such inspiring models. Three different measures of the second kind of TPS-BVPs is applied to assess the robustness, correctness and usefulness of the designed FF-GNN-GAASM. Statistical evaluations through the performance of FF-GNN-GAASM is validated via consistent stability, accuracy and convergence. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Article Citation - WoS: 12Citation - Scopus: 15Numerical Solutions of a Novel Designed Prevention Class in the Hiv Nonlinear Model(Tech Science Press, 2021) Umar, Muhammad; Raja, Muhammad Asif Zahoor; Baleanu, Dumitru; Sabir, ZulqurnainThe presented research aims to design a new prevention class (P) in the HIV nonlinear system, i.e., the HIPV model. Then numerical treatment of the newly formulated HIPV model is portrayed handled by using the strength of stochastic procedure based numerical computing schemes exploiting the artificial neural networks (ANNs) modeling legacy together with the optimization competence of the hybrid of global and local search schemes via genetic algorithms (GAs) and active-set approach (ASA), i.e., GA-ASA. The optimization performances through GA-ASA are accessed by presenting an error-based fitness function designed for all the classes of the HIPV model and its corresponding initial conditions represented with nonlinear systems of ODEs. To check the exactness of the proposed stochastic scheme, the comparison of the obtained results and Adams numerical results is performed. For the convergence measures, the learning curves are presented based on the different contact rate values. Moreover, the statistical performances through different operators indicate the stability and reliability of the proposed stochastic scheme to solve the novel designed HIPV model.Article Citation - WoS: 5Citation - Scopus: 6Multi-Objective Trajectory Planning for Slung-Load Quadrotor System(Ieee-inst Electrical Electronics Engineers inc, 2021) Ergezer, HalitIn this article, multi-objective trajectory planning has been carried out for a quadrotor carrying a slung load. The goal is to obtain non-dominated solutions for path length, mission duration, and dissipated energy cost functions. These costs are optimized by imposing constraints on the slung-load quadrotor system's endpoints, borders, obstacles, and dynamical equations. The dynamic model of a slung-load quadrotor system is used in the Euler-Lagrange formulation. Although the differential flatness feature is mostly used in this system's trajectory planning, a fully dynamic model has been used in our study. A new multi-objective Genetic Algorithm has been developed to solve path planning, aiming to optimize trajectory length, mission time, and energy consumed during the mission. The solution process has a three-phase algorithm: Phase-1 is about randomly generating waypoints, Phase-2 is about constructing the initial non-dominated pool, and the final phase, Phase-3, is obtaining the solution. In addition to conventional genetic operators, simple genetic operators are proposed to improve the trajectories locally. Pareto Fronts have been obtained corresponding to exciting scenarios. The method has been tested, and results have been presented at the end. A comparison of the solutions obtained with MOGA operators and MOPSO over hypervolume values is also presented.Article Citation - WoS: 2Citation - Scopus: 4A New Systematic and Flexible Method for Developing Hierarchical Decision-Making Models(Tubitak Scientific & Technological Research Council Turkey, 2015) Beldek, Ulas; Leblebicioglu, Mehmet Kemal; Lebleb Iciog lu, Mehmet Kemal; Belde, UlaşThe common practice in multilevel decision-making (DM) systems is to achieve the final decision by going through a finite number of DM levels. In this study, a new multilevel DM model is proposed. This model is called the hierarchical DM (HDM) model and it is supposed to provide a flexible way of interaction and information flow between the consecutive levels that allows policy changes in DM procedures if necessary. In the model, in the early levels, there are primary agents that perform DM tasks. As the levels increase, the information associated with these agents is combined through suitable processes and agents with higher complexity are formed to carry out the DM tasks more elegantly. The HDM model is applied to the case study 'Fault degree classification in a 4-tank water circulation system'. For this case study, the processes that connect the lower levels to the higher levels are agent development processes where a special decision fusion technique is its integral part. This decision fusion technique combines the previous level's decisions and their performance indicator suitably to contribute to the improvement of new agents in higher levels. Additionally, the proposed agent development process provides flexibility both in the training and validation phases, and less computational effort is required in the training phase compared to a single-agent development simulation carried out for the same DM task under similar circumstances. Hence, the HDM model puts forward an enhanced performance compared to a single agent with a more sophisticated structure. Finally, model validation and efficiency in the presence of noise are also simulated. The adaptability of the agent development process due to the flexible structure of the model also accounts for improved performance, as seen in the results.Article Citation - WoS: 62Citation - Scopus: 53Design of Stochastic Numerical Solver for the Solution of Singular Three-Point Second-Order Boundary Value Problems(Springer London Ltd, 2021) Baleanu, Dumitru; Shoaib, Muhammad; Raja, Muhammad Asif Zahoor; Sabir, ZulqurnainIn this paper, a novel meta-heuristic computing solver is presented for solving the singular three-point second-order boundary value problems using artificial neural networks (ANNs) optimized by the combined strength of global and local search ability of genetic algorithms (GAs) and interior point algorithm (IPA), i.e., ANN-GA-IPA. The inspiration for presenting this numerical work comes from the intention of introducing a consistent framework that combines the effective features of neural networks optimized with the contexts of soft computing to handle with such challenging systems. Three numerical variants of singular second-order system have been taken to examine the proficiency, robustness, and stability of the designed approach. The comparison of the proposed results of ANN-GA-IPA from available exact solutions shows the good agreement with 5 to 7 decimal places of the accuracy which established worth of the methodology through performance analyses on a single and multiple executions.Article Citation - WoS: 63Citation - Scopus: 72A New Hybrid Algorithm for Continuous Optimization Problem(Elsevier Science inc, 2018) Jafarian, Ahmad; Baleanu, Dumitru; Farnad, BehnamThis paper applies a new hybrid method by a combination of three population base algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Symbiotic Organisms Search (SOS). The proposed method has been inspired from natural selection process and it completes this process in GA by using the PSO and SOS. It tends to minimize the execution time and in addition to reduce the complexity. Symbiotic organisms search is a robust and powerful metaheuristic algorithm which has attracted increasing attention in recent decades. There are three alternative phases in the proposed algorithm: GA, which develops and selects best population for the next phases, PSO, which gets experiences for each appropriate solution and updates them as well and SOS, which benefits from previous phases and performs symbiotic interaction update phases in the real-world population. The proposed algorithm was tested on the set of best known unimodal and multimodal benchmark functions in various dimensions. It has further been evaluated in, the experiment on the clustering of benchmark datasets. The obtained results from basic and non-parametric statistical tests confirmed that this hybrid method dominates in terms of convergence, execution time, success rate. It optimizes the high dimensional and complex functions Rosenbrock and Griewank up to 10(-330) accuracy in less than 3 s, outperforming other known algorithms. It had also applied clustering datasets with minimum intra-cluster distance and error rate. (C) 2017 Elsevier Inc. 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