Person: YILDIZ, BETÜL SULTAN
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YILDIZ
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BETÜL SULTAN
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Publication The harris hawks optimization algorithm, salp swarm algorithm, grasshopper optimization algorithm and dragonfly algorithm for structural design optimization of vehicle components(Carl Hanser Verlag, 2019-08-01) Yıldız, Betül Sultan; Yıldız, Ali Rıza; YILDIZ, BETÜL SULTAN; YILDIZ, ALİ RIZA; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi; 0000-0003-1790-6987; 0000-0001-7592-8733; 0000-0002-7493-2068; AAH-6495-2019; F-7426-2011; AAL-9234-2020There is a growing interest in designing lightweight and low-cost vehicles. In this research, the Harris hawks optimization algorithm (the HHO), the salp swarm algorithm (SSA), the grasshopper optimization algorithm(GOA), and the dragonfly algorithm (DA) are introduced to solve shape optimization problems in the automotive industry. This research is the first application of the HHO, the SSA, the GOA, and the DA to shape design optimization problems in the literature. In this paper, the HHO, the SSA, and the DA algorithms are used for shape optimization of a vehicle brake pedal to prove how the HHO, the SSA, the GOA, and the DA can be used for solving shape optimization problems. The results show the ability of the HHO, the SSA, the GOA, and the DA to design better optimal components.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; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü.; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine 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 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; Bursa Uludağ Üniversitesi/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 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; Bursa Uludağ Üniversitesi/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 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; Bursa Uludağ Üniversitesi/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 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; Bursa Uludağ Üniversitesi/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 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; Bursa Uludağ Üniversitesi/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 new chaotic levy flight distribution optimization algorithm for solving constrained engineering problems(Wiley, 2022-03-23) Kumar, Sumit; Pholdee, Nantiwat; Bureerat, Sujin; Sait, Sadiq M.; YILDIZ, ALİ RIZA; YILDIZ, BETÜL SULTAN; 0000-0002-7493-2068; 0000-0003-1790-6987; AAL-9234-2020; F-7426-2011This work proposed a new metaheuristic dubbed as Chaotic Levy flight distribution (CLFD) algorithm, to address physical world engineering optimization problems that incorporate the chaotic maps in the elementary Levy flight distribution (LFD). Hybridization aims to increase the LFD rate of convergence while also providing a problem-free optimization approach. The proposed methodology is investigated for five case studies of constrained optimization issues followed by shape optimization of structural design. The outcomes from the CFLD algorithm are further contrasted with its fundamental version and other distinguished recently introduced algorithms. The computational analysis illustrates the dominance of CLFD over other considered optimizers. Moreover, the present investigation shows that CLFD is a robust technique that can efficiently find optimal mechanical design problems with a proper chaotic map selection.Publication A novel chaotic runge kutta optimization algorithm for solving constrained engineering problems(Oxford Univ Press, 2022-12-01) Yıldız, Betül Sultan; Mehta, Pranav; Panagant, Natee; Mirjalili, Seyedali; Yıldız, Ali Riza; YILDIZ, BETÜL SULTAN; YILDIZ, ALİ RIZA; Bursa Uludağ Üniversitesi/Makine Mühendisliği Bölümü; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği Bölümü; F-7426-2011; AAL-9234-2020This study proposes a novel hybrid metaheuristic optimization algorithm named chaotic Runge Kutta optimization (CRUN). In this study, 10 diverse chaotic maps are being incorporated with the base Runge Kutta optimization (RUN) algorithm to improve their performance. An imperative analysis was conducted to check CRUN's convergence proficiency, sustainability of critical constraints, and effectiveness. The proposed algorithm was tested on six well-known design engineering tasks, namely: gear train design, coupling with a bolted rim, pressure vessel design, Belleville spring, and vehicle brake-pedal optimization. The results demonstrate that CRUN is superior compared to state-of-the-art algorithms in the literature. So, in each case study, CRUN was superior to the rest of the algorithms and furnished the best-optimized parameters with the least deviation. In this study, 10 chaotic maps were enhanced with the base RUN algorithm. However, these chaotic maps improve the solution quality, prevent premature convergence, and yield the global optimized output. Accordingly, the proposed CRUN algorithm can also find superior aspects in various spectrums of managerial implications such as supply chain management, business models, fuzzy circuits, and management models.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; Bursa Uludağ Üniversitesi/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.