Person: YILDIZ, BETÜL SULTAN
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YILDIZ
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BETÜL SULTAN
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Publication A comparative study of state-of-the-art metaheuristics for solving many-objective optimization problems of fixed wing unmanned aerial vehicle conceptual design(Springer, 2023-04-11) Anosri, Siwakorn; Panagant, Natee; Champasak, Pakin; Bureerat, Sujin; Thipyopas, Chinnapat; Kumar, Sumit; Pholdee, Nantiwat; Yıldız, Betül Sultan; Yıldız, Ali Riza; YILDIZ, ALİ RIZA; YILDIZ, BETÜL SULTAN; Mühendislik Fakültesi; Makine Mühendisliği Bölümü; 0000-0001-7592-8733 ; AAH-6495-2019; F-7426-2011The complexity of aircraft design problems increases with many objectives and diverse constraints, thus necessitating effective optimization techniques. In recent years many new metaheuristics have been developed, but their implementation in the design of the aircraft is limited. In this study, the effectiveness of twelve new algorithms for solving unmanned aerial vehicle design issues is compared. The optimizers included Differential evolution for multi-objective optimization, Many-objective nondominated sorting genetic algorithm, Knee point-driven evolutionary algorithm for many-objective optimization, Reference vector guided evolutionary algorithm, Multi-objective bat algorithm with nondominated sorting, multi-objective flower pollination algorithm, Multi-objective cuckoo search algorithm, Multi-objective multi-verse optimizer, Multi-objective slime mould algorithm, Multi-objective jellyfish search algorithm, Multi-objective evolutionary algorithm based on decomposition and Self-adaptive many-objective meta-heuristic based on decomposition. The design problems include four many-objective conceptual designs of UAV viz. Conventional, Conventional with winglet, Twin boom and Canard, which are solved by all the optimizers employed. Widely used Hypervolume and Inverted Generational Distance metrics are considered to evaluate and compare the performance of examined algorithms. Friedman's rank test based statistical examination manifests the dominance of the DEMO optimization technique over other compared techniques and exhibits its effectiveness in solving aircraft conceptual design problems. The findings of this work assist in not only solving aircraft design problems but also facilitating the development of unique algorithms for such challenging issues.Publication A novel hybrid fick's law algorithm-quasi oppositional-based learning algorithm for solving constrained mechanical design problems(Walter De Gruyter Gmbh, 2023-09-13) Mehta, Pranav; Sait, Sadiq M.; Yıldız, Ali Rıza; YILDIZ, ALİ RIZA; Yıldız, Betül Sultan; YILDIZ, BETÜL SULTAN; Mühendislik Fakültesi; Makina Mühendisliği Bölümü; AAL-9234-2020; F-7426-2011In this article, a recently developed physics-based Fick's law optimization algorithm is utilized to solve engineering optimization challenges. The performance of the algorithm is further improved by incorporating quasi-oppositional-based techniques at the programming level. The modified algorithm was applied to optimize the rolling element bearing system, robot gripper, planetary gear system, and hydrostatic thrust bearing, along with shape optimization of the vehicle bracket system. Accordingly, the algorithm realizes promising statistical results compared to the rest of the well-known algorithms. Furthermore, the required number of iterations was comparatively less required to attain the global optimum solution. Moreover, deviations in the results were the least even when other optimizers provided better or more competitive results. This being said that this optimization algorithm can be adopted for a critical and wide range of industrial and real-world challenges optimization.Publication Hunger games search algorithm for global optimization of engineering design problems(Walter De Gruyter Gmbh, 2022-04-26) Mehta, Pranav; Yıldız, Betül Sultan; Sait, Sadiq M.; Yıldız, Ali Rıza; YILDIZ, BETÜL SULTAN; YILDIZ, ALİ RIZA; Mühendislik Fakültesi; Elektrik ve Enerji Bölümü; F-7426-2011; AAL-9234-2020The modernization in automobile industries has been booming in recent times, which has led to the development of lightweight and fuel-efficient design of different automobile components. Furthermore, metaheuristic algorithms play a significant role in obtaining superior optimized designs for different vehicle components. Hence, a hunger game search (HGS) algorithm is applied to optimize the automobile suspension arm (SA) by reduction of mass vis-a-vis volume. The performance of the HGS algorithm was accomplished by comparing the achieved results with the well-established metaheuristics (MHs), such as salp swarm optimizer, equilibrium optimizer, Harris Hawks optimizer (HHO), chaotic HHO, slime mould optimizer, marine predator optimizer, artificial bee colony optimizer, ant lion optimizer, and it was found that the HGS algorithm is able to pursue the best optimized solution subjecting to critical constraints. Moreover, the HGS algorithm can realize the least weight of the SA subjected to maximum stress values. Hence, the adopted algorithm can be found robust in terms of obtaining the best global optimum solution.Publication A new hybrid artificial hummingbird-simulated annealing algorithm to solve constrained mechanical engineering problems(Walter de Gruyter Gmbh, 2022-07-26) Yıldız, Betül Sultan; Mehta, Pranav; Sait, Sadiq M.; Panagant, Natee; Kumar, Sumit; Yıldız, Ali Rıza; YILDIZ, BETÜL SULTAN; YILDIZ, ALİ RIZA; Mühendislik Fakültesi; Makine Mühendisliği Bölümü; AAL-9234-2020; F-7426-2011Nature-inspired algorithms known as metaheuristics have been significantly adopted by large-scale organizations and the engineering research domain due their several advantages over the classical optimization techniques. In the present article, a novel hybrid metaheuristic algorithm (HAHA-SA) based on the artificial hummingbird algorithm (AHA) and simulated annealing problem is proposed to improve the performance of the AHA. To check the performance of the HAHA-SA, it was applied to solve three constrained engineering design problems. For comparative analysis, the results of all considered cases are compared to the well-known optimizers. The statistical results demonstrate the dominance of the HAHA-SA in solving complex multi-constrained design optimization problems efficiently. Overall study shows the robustness of the adopted algorithm and develops future opportunities to optimize critical engineering problems using the HAHA-SA.Publication A novel generalized normal distribution optimizer with elite oppositional based learning for optimization of mechanical engineering problems(Gmbh, 2023-02-23) Mehta, Pranav; Yıldız, Betuel Sultan; Pholdee, Nantiwat; Kumar, Sumit; Riza Yıldız, Ali; Sait, Sadiq M. M.; Bureerat, Sujin; YILDIZ, BETÜL SULTAN; YILDIZ, ALİ RIZA; Mühendislik Fakültesi; Makine Mühendisliği Bölümü; F-7426-2011; AAH-6495-2019Optimization of engineering discipline problems are quite a challenging task as they carry design parameters and various constraints. Metaheuristic algorithms can able to handle those complex problems and realize the global optimum solution for engineering problems. In this article, a novel generalized normal distribution algorithm that is integrated with elite oppositional-based learning (HGNDO-EOBL) is studied and employed to optimize the design of the eight benchmark engineering functions. Moreover, the statistical results obtained from the HGNDO-EOBL are collated with the data obtained from the well-established algorithms such as whale optimizer, salp swarm optimizer, LFD optimizer, manta ray foraging optimization algorithm, hunger games search algorithm, reptile search algorithm, and INFO algorithm. For each of the cases, a comparison of the statistical results suggests that HGNDO-EOBL is superior in terms of realizing the prominent values of the fitness function compared to established algorithms. Accordingly, the HGNDO-EOBL can be adopted for a wide range of engineering optimization problems.Publication Crash performance optimization of vehicle elements using arithmetic optimization algorithm(Gazi Univ, 2023-09-01) Yıldız, Betül Sultan; YILDIZ, BETÜL SULTAN; Mühendislik Fakültesi; Makina Mühendisliği Bölümü; AAL-9234-2020In this study, the newly developed arithmetic optimization algorithm is used for the first time in the literature for the optimum design of automobile components exposed to crash. In conjunction with the enhancement of crash and NVH characteristics in the optimization study, the design objective is to minimize vehicle weight. For vehicle performance analysis, a comprehensive automobile structure, faithful to the original, was used. Both multiple crash analysis and vibration analysis of finite element models were performed to examine full, offset and side impact crash scenarios with natural frequencies. The evaluated structural responses are estimated based on the radial basis functions technique. An arithmetic optimization algorithm is used to optimize the vehicle mass under various nonlinear crash and vibration limits. The results revealed the effectiveness of the arithmetic optimization algorithm in the optimum design of vehicle components.Publication Gradient-based optimizer for economic optimization of engineering problems(Walter De Gruyter Gmbh, 2022-05-25) Mehta, Pranav; Sait, Sadiq M.; Yıldız, Betül Sultan; YILDIZ, BETÜL SULTAN; Yıldız, Ali Rıza; YILDIZ, ALİ RIZA; Mühendislik Fakültesi; Makine Mühendisliği Bölümü; 0000-0002-4796-0581; 0000-0003-1790-6987; F-7426-2011; AAL-9234-2020; B-3604-2008Optimization of the heat recovery devices such as heat exchangers (HEs) and cooling towers is a complex task. In this article, the widely used fin and tube HE (FTHE) is optimized in terms of the total costs by the novel gradient-based optimization (GBO) algorithm. The FTHE s have a cylindrical tube with transverse or longitudinal fin enhanced on it. For this study, various constraints and design variables are considered, with the total cost as the objective function. The study reveals that the GBO provides promising results for the present case study with the highest success rate. Also, the comparative results suggest that GBO is the robust optimizer in terms of the best-optimized values of the fitness function vis-a-vis design variables. This study builds the future implications of the GBO in a wide range of engineering optimization fields.Publication A novel hybrid flow direction optimizer-dynamic oppositional based learning algorithm for solving complex constrained mechanical design problems(Walter de Gruyter Gmbh, 2023-01-27) Yıldız, Betül Sultan; Pholdee, Nantiwat; Mehta, Pranav; Sait, Sadiq M.; Kumar, Sumit; Bureerat, Sujin; Yıldız, Ali Rıza; YILDIZ, BETÜL SULTAN; YILDIZ, ALİ RIZA; Makine Mühendisliği Bölümü; AAL-9234-2020; F-7426-2011In this present work, mechanical engineering optimization problems are solved by employing a novel optimizer (HFDO-DOBL) based on a physics-based flow direction optimizer (FDO) and dynamic oppositional-based learning. Five real-world engineering problems, viz. planetary gear train, hydrostatic thrust bearing, robot gripper, rolling bearing, and multiple disc clutch brake, are considered. The computational results obtained by HFDO-DOBL are compared with several newly proposed algorithms. The statistical analysis demonstrates the HFDO-DOBL dominance in finding optimal solutions relatively and competitiveness in solving constraint design optimization problems.Publication Optimum design of a seat bracket using artificial neural networks and dandelion optimization algorithm(Walter de Gruyter Gmbh, 2023-10-13) Erdaş, Mehmet Umut; Kopar, Mehmet; Yıldız, Betül Sultan; Yıldız, Ali Rıza; Erdaş, Mehmet Umut; Kopar, Mehmet; YILDIZ, BETÜL SULTAN; YILDIZ, ALİ RIZA; Mühendislik Fakültesi; Otomotiv Mühendisliği Bölümü; 0000-0003-1790-6987; AAH-6495-2019; F-7426-2011; CNV-1200-2022; DBQ-9849-2022Nature-inspired metaheuristic algorithms are gaining popularity with their easy applicability and ability to avoid local optimum points, and they are spreading to wide application areas. Meta-heuristic optimization algorithms are used to achieve an optimum design in engineering problems aiming to obtain lightweight designs. In this article, structural optimization methods are used in the process of achieving the optimum design of a seat bracket. As a result of topology optimization, a new concept design of the bracket was created and used in shape optimization. In the shape optimization, the mass and stress values obtained depending on the variables, constraint, and objective functions were created by using artificial neural networks. The optimization problem based on mass minimization is solved by applying the dandelion optimization algorithm and verified by finite element analysis.Publication Aircraft conceptual design using metaheuristic-based reliability optimisation(Elsevier France-Editions Scientifiques Medicales Elsevier, 2022-08-19) Champasak, Pakin; Panagant, Natee; Pholdee, Nantiwat; Vio, Gareth A.; Bureerat, Sujin; Yıldız, Betül Sultan; Yıldız, Ali Rıza; YILDIZ, BETÜL SULTAN; YILDIZ, ALİ RIZA; Mühendislik Fakültesi; Makine Mühendisliği Bölümü; 0000-0002-7493-2068; F-7426-2011; AAL-9234-2020The reliability optimisation methodology is developed to solve a conceptual design problem of a fixed-wing Unmanned Aerial Vehicle (UAV). The reliability quantification is based on the most probable point (MPP) concept, leading to a double-loop optimisation problem. The design problem is formulated as an outer-loop multiobjective optimisation for the main design problem and an inner-loop optimisation for estimating a reliability index (beta) of a design solution. The goal of the outer-loop optimisation is to minimise the aircraft take-off weight, and simultaneously maximise beta. The aerodynamic and stability properties of an aircraft solution are calculated by a vortex lattice method (VLM), while various types of empirical weight methods are obtained. The design problem is set up to have uncertainties in calculating aircraft empty weight and aerodynamic coefficients and derivatives. For the inner loop optimisation, the MPP is used for approximating the reliability index and probability of failure (pf). Multiobjective Meta-heuristic with Iterative Parameter Distribution Estimation (MMIPDE) and Success-History based Adaptive Differential Evolution (SHADE) are used for solving the outer- and inner-loop optimisations respectively. Four parameters setting strategies for running metaheuristics are proposed for use with the proposed metaheuristic-based reliability optimisation. The comparative results reveal that the best dynamic parameter setting from this study can reduce runtime by 22.5% compared to the traditional metaheuristic run while maintaining competitive results. (C) 2022 Elsevier Masson SAS. All rights reserved.