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Öğe 2D hyperchaotic Styblinski-Tang map for image encryption and its hardware implementation(Springer Link, 2024) Üstün, Deniz; Erkan, Uğur; Toktaş, Abdurrahim; Lai, Qiang; Yang, LiangA novel 2D chaotic system is presented, which is inspired by Styblinski Tang (ST) function employed as optimization test function. It is a challenge function because of having many local optima. The performance of the chaotic system namely 2D Styblinski Tang (2D-ST) map is corroborated through an extensive comparison with the literature in terms of the sensitive chaos metrics as well as its randomness is verified over TestU0. The 2D-ST map manifests the best hyperchaotic behavior due to higher ergodicity and complexity characteristics. Moreover, the 2D-ST map is implemented to a microcontroller hardware, and it is seen that the results manifests that the proposed 2D-ST can be a potential practical candidate thanks to excellent hyperchaotic performance.Öğe A symbiotic organisms search algorithm-based design optimization of constrained multi-objective engineering design problems(Emerald Insight, 2021) Üstün, Deniz; Carbas, Serdar; Toktaş, AbdurrahimPurpose In line with computational technological advances, obtaining optimal solutions for engineering problems has become attractive research topics in various disciplines and real engineering systems having multiple objectives. Therefore, it is aimed to ensure that the multiple objectives are simultaneously optimized by considering them among the trade-offs. Furthermore, the practical means of solving those problems are principally concentrated on handling various complicated constraints. The purpose of this paper is to suggest an algorithm based on symbiotic organisms search (SOS), which mimics the symbiotic reciprocal influence scheme adopted by organisms to live on and breed within the ecosystem, for constrained multi-objective engineering design problems. Design/methodology/approach Though the general performance of SOS algorithm was previously well demonstrated for ordinary single objective optimization problems, its efficacy on multi-objective real engineering problems will be decisive about the performance. The SOS algorithm is, hence, implemented to obtain the optimal solutions of challengingly constrained multi-objective engineering design problems using the Pareto optimality concept. Findings Four well-known mixed constrained multi-objective engineering design problems and a real-world complex constrained multilayer dielectric filter design problem are tackled to demonstrate the precision and stability of the multi-objective SOS (MOSOS) algorithm. Also, the comparison of the obtained results with some other well-known metaheuristics illustrates the validity and robustness of the proposed algorithm. Originality/value The algorithmic performance of the MOSOS on the challengingly constrained multi-objective multidisciplinary engineering design problems with constraint-handling approach is successfully demonstrated with respect to the obtained outperforming final optimal designs.Öğe An image encryption scheme based on an optimal chaotic map derived by multi-objective optimization using ABC algorithm(Springer Link, 2021) Toktaş, Abdurrahim; Üstün, Deniz; Erkan, UğurA novel optimal chaotic map (OCM) is proposed for image encryption scheme (IES). The OCM is constructed using a multi-objective optimization strategy through artificial bee colony (ABC) algorithm. An empirical model for the OCM with four unknown variables is first constituted, and then, these variables are optimally found out using ABC for minimizing the multi-objective function composed of the information entropy and Lyapunov exponent (LE) of the OCM. The OCM shows better chaotic attributes in the evaluation analyses using metrics such as bifurcation, 3D phase space, LE, permutation entropy (PE) and sample entropy (SE). The encrypting performance of the OCM is demonstrated on a straightforward IES and verified by various cryptanalyses that compared with many reported studies, as well. The main superiority of the OCM over the studies based on optimization is that it does not require any optimization in the encrypting operation; thus, OCM works standalone in the encryption. However, those reported studies use ciphertext images obtained through encrypting process in every cycle of optimization algorithm, resulting in long processing time. Therefore, the IES with OCS is faster than the others optimization-based IES. Furthermore, the proposed IES with the OCM manifests satisfactory outcomes for the compared results with the literature.Öğe An optimized surrogate model using differential evolution algorithm for computing parameters of antennas(Wiley, 2022) Üstün, Deniz; Toktaş, Feyza; Toktaş, AbdurrahimIn this study, a method based on surrogate model (SM) for computational analysis of antenna parameters such as the resonant frequency (RF) and bandwidth (BW) is presented. Moreover, it is attempted to optimize the SM using evolutionary optimization algorithms in order to further improve the accuracy of the SM. In the conventional computational approaches, the weighting vectors of the SM have been analytically determined. We have optimally achieved the weighting vectors of the SM through differential evolution (DE) and particle swarm optimization (PSO) algorithms. The capabilities of the algorithms are hereby compared with each other. The methodology is applied to the analysis of rectangular microstrip antenna (RMA), including a number of 33 measured RMAs with different geometrical and electrical parameters. From the total number of RMAs, 27 and 6 RMAs are, respectively, used in the construction and the test of the SM. Furthermore, the SM is verified through a comparison with the literature in terms of total absolute errors (TAEs). The results show that the SM with DE computes the most accurate RF and BW with the TAEs of 0.0099 GHz and 0.131%, respectively. The accuracy of the SM is further raised by 78%, thanks to the optimization of SM with DE. Therefore, a novel computational analysis method based on SM is implemented to computation of an antenna parameter with higher accuracy, and SM is successfully optimized by DE. The proposed method is able to easily implement to the stringent engineering problems based on simulated or measured data for computer-aided design (CAD).Öğe Design Optimization of Multi-objective Structural Engineering Problems Via Artificial Bee Colony Algorithm(Springer Link, 2021) Carbas, Serdar; Üstün, Deniz; Toktaş, AbdurrahimThe construction sector constitutes a significant portion of global gross national expenditures with huge financial budget requirements and provides employment for more than one hundred million people. Besides, considering that people spend more than 80% of their time indoors today, it is necessary to make optimal structure designs. This requirement stems from the inadequacy of existing structures in the face of today's changing conditions. Indeed, realistic design optimization of the structures can be done not only by taking into account a single objective but also considering a number of structural criteria. It means that there is inherent multi-purpose in most structural design optimization problems. Thus, it is very difficult engineering task to solve these kinds of problems, as it is necessary to optimize multiple purposes simultaneously to obtain optimal designs. With the help of the improvisation in optimization techniques used for multi-objective structural engineering design, algorithms are provided to achieve the optimal designs by creating a strong synergy between the structural requirements and constraints mentioned in the design specifications. The recent addition to this trend is so-called Artificial Bee Colony (ABC) algorithm which simulates the nectar searching ability of the bees in nature for nutrition. In this chapter, an optimal design algorithm via ABC is proposed in order to obtain the optimum design of multi-objective structural engineering design problems. The applications in design examples have shown the robustness, effectiveness, and reliability of ABC in attaining the design optimization of multi-objective constrained structural engineering design problems.Öğe Design optimization of multilayer microwave filter using differential evolution algorithm(Institute of Electrical and Electronics Engineers (IEEE), 2022) Toktaş, Abdurrahim; Üstün, DenizThree types of multilayer microwave filter (MMF) which are low-pass (LP), high-pass (HP) and band-pass (BP) are designed through a frequency-dependent material using Pareto-based multi-objective differential evolution (DE) (MODE) algorithm. To this end, a dual-objective electromagnetic (EM) model taking into account oblique incident wave angle with transverse electric (TE) or transverse magnetic (TM) polarization is constructed, which is based on total reflection (TR) for the passing and stopping bands. The global optimal designs (GODs) are selected from the Pareto optimal designs (PODs) by considering the trade-off among the objective values. The frequency and angular behavior of the MMFs are comparatively studied. The designed MMF are near ideal characteristic thanks to MODE having effective and powerful multi-objective strategy.Öğe Hyperparameter optimization of deep CNN classifier for plant species identification using artificial bee colony algorithm(Springer Link, 2023) Erkan, Uğur; Toktaş, Abdurrahim; Üstün, DenizTailoring a deep convolutional neural network (CNN) for an implementation is a tedious and time-consuming task especially in image identification. In this study, an optimization scheme based on artificial bee colony (ABC) algorithm so-called optimal deep CNN (ODC) classifier for hyperparameter optimization of deep CNN is proposed for plant species identification. It is implemented to a ready-made leaf dataset namely Folio containing #637 images with 32 different plant species. The images are undergone various image preprocessing such as scaling, segmentation and augmentation so as to improve the efficacy of the ODC classifier. Therefore, the dataset is augmented from #637 to #15,288 leaf images whose #12,103 images is allocated for training phase and the remainder for testing the ODC. Moreover, a validation process on 20% of the training dataset is performed along with the training phase in both optimization and classification stages. The accuracy and loss performance of the ODC are examined over the training and validation results. The achieved ODC is verified through the test phase as well as by a comparison with the results in the literature in terms of performance evaluation metrics such as accuracy, sensitivity, specificity and F1-score. In order to further corroborate the proposed scheme, it is even subjected to a benchmark with optimization-based studies such as genetic, particle swarm and firefly algorithms through MNIST digit-image dataset. The ODC identifies the leaf images and digit-images with the best accuracy of 98.99% and 99.21% surpassing the state of the arts. Therefore, the proposed ODC is effective and useful in achieving an optimal CNN thanks to ABC algorithm.Öğe Implementation of Flower Pollination Algorithm to the Design Optimization of Planar Antennas(Springer Link, 2021) Toktaş, Abdurrahim; Üstün, Deniz; Carbas, SerdarFlower pollination algorithm (FPA) is an outstanding metaheuristic optimization approach among the recently emerged nature-inspired algorithms. It is built on pollination nature of the flowers, classifying into two categories: biotic and abiotic pollinations. It is observed that the performance of FPA has been well demonstrated through diverse engineering design problems, whereas its efficacy in the design optimization of planar antennas, which are the most important concealed elements in the wireless communication systems, is remained curious in the engineering research topics. In this chapter, FPA is hence applied to the design of planar antennas in order to optimize their shapes and dimensions for the objective function based on resonant bandwidth. The design optimization is carried out through a cooperating platform constituted in this work, communicating MATLAB® with a full-wave simulator named Hyperlynx® 3D EM. Four different planar antennas are hereby designed and optimized for modern wireless communication across a step-by-step procedure. The finally optimized antenna geometries are provided with elaborate dimensions and their performance parameters such as operating frequency band, radiation gain pattern, and peak gain are examined. Therefore, it is shown off that FPA is also effective and successful in the design optimization of planar antennas.Öğe Index-based simultaneous permutation-diffusion in image encryption using two-dimensional price map(Springer Link, 2024) Lai, Qiang; Zhang, Hui; Üstün, Deniz; Erkan, Uğur; Toktaş, AbdurrahimThis paper proposes an index-based simultaneous permutation-diffusion image encryption algorithm (ISPD-IEA) based on chaos theory and a permutation-diffusion coupled encryption mechanism. The proposed method introduces a novel two-dimensional (2D) Price map derived from the Price function and classical maps, exhibiting superior chaotic dynamical properties compared to existing alternatives. By integrating the permutation-diffusion process, ISPD-IEA effectively diffuses minor changes in pixel values while altering their positions, enhancing both encryption efficiency and resistance against differential analysis attacks. Experimental results and thorough security analysis confirm the outstanding security and high encryption efficiency of ISPD-IEA. The algorithm not only achieves excellent encryption performance but also demonstrates its ability to resist various attacks commonly encountered in image encryption scenarios.Öğe Introduction and overview: nature-inspired metaheuristic algorithms for engineering optimization applications(Springer Link, 2021) Carbaş,; Toktaş, Abdurrahim; Üstün, DenizThis chapter provides an introduction and overview of this book content titled Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications. This book supplies both theoretical standpoints and practical examples for engineering researchers and/or practitioners taking roles in obtaining optimum designs of engineering applications from various disciplines through the nature-inspired metaheuristic algorithms. Thus, the touchstone of the book is junction of the close contact between theory and practice. The chapters of the book are built on theoretical perspectives through the selected design examples picked out from engineering practices. From this aspect, this book covers different illustrative topics under two different parts which are Part I: Civil and Structural Engineering and Part II: Electrical and Electronics, Computer, and Communication Engineering including variety of nature-inspired metaheuristic algorithms’ applications for numerous engineering design optimization problems. Hence, this book not only provides an elaborative practical and theoretical guide for practitioners, but also leads opportunity for inquiring the both relatively conventional and contemporary metaheuristic algorithms for researches from different fields of engineering disciplines.Öğe Modified artificial bee colony algorithm with differential evolution to enhance precision and convergence performance(Elsevier, 2022) Üstün, Deniz; Toktaş, Abdurrahim; Erkan, Uğur; Akdağlı, AliArtificial bee colony (ABC) and differential evolution (DE) are the most powerful and operative meta-heuristic algorithms inspired by the nature. Although both algorithms are successful, their successes vary from phase to phase, i.e. while ABC is better in the exploration ability, DE is well in the exploitation capability. Because the diversity of mutation and exponential crossover operators is prominently better than that of onlooker bee; in this study, the exploitation ability of ABC is enhanced by replacing the onlooker bee operator with those of mutation and the crossover phases of DE in order to increase the accuracy and speed up the convergence. We hereby introduce a novel modified algorithm denoted “modified ABC by DE” (mABC). The precision performance of mABC is verified through 20 classical benchmark functions and CEC 2014 test suit by a comprehensive comparison with recent ABC variants and hybrids for 30 and 50 dimensions. The results are interpreted using various statistical evaluations such as Wilcoxon, Friedman, and Nemenyi tests. Moreover, mABC is comparatively examined over convergence plots. In concise, the mean ranks of mABC are 1.4 and 2.3 for classical benchmark functions and CEC 2014, respectively. mABC outperforms the other variants averagely for 14 of 20 classical benchmark functions and 24 of 30 CEC 2014 functions. The results manifest that the proposed mABC is a robust and reliable algorithm as well as better than the existing ABC variants and hybrids with regard to high optimization performance like precision and convergenceÖğe Multi-objective Optimization of Engineering Design Problems Through Pareto-Based Bat Algorithm(Springer Link, 2021) Üstün, Deniz; Carbas, Serdar; Toktaş, AbdurrahimAlthough various optimization methods for solving single-objective problems have been developed in the last few decades, these methods have lost their eligibility due to the fact that today’s engineering problems are toward multiple objective optimization problems, in real applications. For single-objective optimization problems, for example, in case of a minimization problem, this value is the decision vector giving the smallest objective that can be achieved within the specified constraints. Hence the minimum decision vector within all possible (feasible) solution vectors is the so-called optimal solution and/or optimal design. However, in multi-objective optimization problems, since a different objective value is generated against each decision vector, the superiority of the solutions over each other is determined by considering the trade-off among the objective values. Therefore, the solution of multi-objective optimization problems, unlike single-objective problems, is a set of vectors rather than a single decision vector. In multi-objective optimization problems, especially if there are intricate objectives, the computational cost of the problem increases. In other words, while synchronously trying to maximize one of the objectives and to minimize another one makes it difficult to find the global optimum design. One of the important techniques used in multi-objective optimization problems is Pareto optimality which enables to select the global optimum solution taking into account the trade-off among all objectives. In this context, using of derivative-based methods has decreased, but the use of metaheuristic methods has increased due to the rapid availability of global optimum solution. This is because the improvements in the field of optimization are progressing in proportion to technology and varying according to the needs. In this chapter, one of the recent metaheuristic optimization methods based on swarm intelligence that is so-called a Pareto-based bat algorithm inspired by the behavior of determining the direction and distance of an object using the echo of the sound called the echolocation of bats is used in order to obtain optimum solutions for multi-objective engineering design problems. In this regard, a four-bar planar truss, a real-sized welded steel beam as well as a multi-layer radar absorber are selected as multi-objective engineering design optimization problems. In case the obtained results (optimal designs) are examined, the potency and the reliability of the proposed multi-objective Pareto-based bat algorithm are demonstrated.Öğe Multiobjective Design of 2D Hyperchaotic System Using Leader Pareto Grey Wolf Optimizer(Institute of Electrical and Electronics Engineers Inc., 2024) Toktaş, Abdurrahim; Erkan, Uğur; Üstün, Deniz; Lai, QiangA chaotic system is a mathematical model exhibiting random and unpredictable behavior. However, existing chaotic systems suffer from suboptimal parameters regarding chaotic indicators. In this study, a novel leader Pareto grey wolf optimizer (LP-GWO) is proposed for multiobjective (MO) design of 2D parametric hyperchaotic system (2D-PHS). The MO capability of LP-GWO is improved by integrating a LP solution within the Pareto optimal set. The effectiveness of LP-GWO is corroborated through a comparison with regular MO versions of grey wolf optimizer (GWO), artificial bee colony, particle swarm optimization, and differential evolution. Additionally, the validation extends to the exploration of LP-GWO's performance across four variants of the 2D-PHS optimized by the compared algorithms. A 2D-PHS model with eight parameters is conceived and then optimized using LP-GWO by ensuring tradeoff between two objectives: Lyapunov exponent (LE) and Kolmogorov entropy (KE). A globally optimal design is chosen for freely improving the two objectives. The chaotic performance of 2D-PHS significantly outperforms existing systems in terms of precise chaos indicators. Therefore, the 2D-PHS has the best ergodicity and erraticity due to optimal parameters provided by LP-GWO.Öğe Parameter optimization of chaotic system using Pareto-based triple objective artificial bee colony algorithm(Springer London Ltd, 2023) Toktaş, Abdurrahim; Erkan, Uğur; Üstün, Deniz; Wang, XingyuanChaotic map is a kind of discrete chaotic system. The existing chaotic maps suffer from optimal parameters in terms of chaos measurements. In this study, a novel approach of optimization of parametric chaotic map (PCM) using triple objective optimization is presented for the first time. A PCM with six parameters is first conceived and then optimized using Pareto-based triple objective artificial bee colony (PT-ABC) algorithm. Pareto optimality is employed to catch the trade-off among the objectives: Lyapunov exponent (LE), sample entropy (SE), and Kolmogorov entropy (KE). A global optimal design including the six parameters is selected for minimizing the reciprocal of the three objectives independently. The chaotic performance of PCM is verified through an evaluation with bifurcation diagram, attractor, LE, SE, KE, and correlation dimension. The results are also validated by comparison with those of which reported elsewhere. Furthermore, the applicability of PCM is examined over image encryption and the results are compared with existing chaos-based IEs. Therefore, the PCM manifests the best ergodicity and complexity thanks to its PT-ABC algorithm.
