Browsing by Author "Mehta, Pranav"

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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-7426-2011
Nature-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.
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-9234-2020
This 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.
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-6495-2019
Optimization 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.
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-7426-2011
In 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.
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-7426-2011
In 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.
African vultures optimization algorithm for optimization of shell and tube heat exchangers
(Walter De Gruyter Gmbh, 2022-08-26) Mehta, Pranav; Sait, Sadiq M.; Gürses, Dildar; GÜRSES, DİLDAR; Yıldız, Ali Riza; YILDIZ, ALİ RIZA; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi.; 0000-0002-4796-0581; 0000-0003-1790-6987; F-7426-2011
Nature-inspired optimization algorithms named meta-heuristics are found to be versatile in engineering design fields. Their adaptability is also used in various areas of the Internet of things, structural design, and thermal system design. With the very rapid progress in industrial modernization, waste heat recovery from the power generating and thermal engineering organization is an imperative key point to reduce the emission and support the government norms. However, the heat exchanger is the component applied in various heat recovery processes. Out of the available designs, shell and tube heat exchangers (SHTHEs) are the most commonly adopted for the heat recovery process. Hence, cost minimization is the major aspect while designing the heat exchanger confirming various constraints and optimized design variables. In this study, cost minimization of the SHTHE is performed by applying a novel metaheuristic algorithm which is the African vultures optimization algorithm (AVOA). Adopting the AVOA for the best-optimized value (least cost of heat exchanger) and the design parameters are realized, confirming all the constraints. It was found that the AVOA is able to pursue the best results among the rest of them and can be used for the cost optimization of the plate-fin and tube-fin heat exchanger case studies.
Cheetah optimization algorithm for optimum design of heat exchangers
(Walter De Gruyter Gmbh, 2023-06-30) Sait, Sadiq M.; Mehta, Pranav; YILDIZ, ALİ RIZA; Gürses, Dildar; GÜRSES, DİLDAR; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi.; Bursa Uludağ Üniversitesi/Gemlik Asım Kocabıyık Meslek Yüksekokulu.; 0000-0002-4796-0581; 0000-0003-1790-6987; F-7426-2011; JCN-8328-2023
Thermal system optimization is always a challenging task due to several constraints and critical concepts of thermo-hydraulic aspects. Heat exchangers are one of those devices that are widely adopted in thermal industries for various applications such as cryogenics, heat recovery, and heat transfer applications. According to the flow configurations and enhancement of fins, the heat exchangers are classified as plate-fin heat exchangers, shell and tube heat exchangers, and tube-fin heat exchangers. This article addresses the economic optimization challenge of plate-fin heat exchangers using cheetah optimization (CO) algorithm. The design variables were optimized using the CO algorithm, and statistical results were compared with eight well-established algorithms. The study revealed that the cheetah algorithm is prominent in terms of realizing minimizing the overall cost of the plate-fin heat exchanger with a 100 % of success rate. Furthermore, the study suggests adopting the cheetah optimizer for solving optimization challenges in different fields.
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-3604-2008
Optimization 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.
Manta ray foraging optimization algorithm and hybrid Taguchi salp swarm-Nelder-Mead algorithm for the structural design of engineering components
(Gmbh, 2022-05-25) Yıldız, Ali Rıza; Mehta, Pranav; YILDIZ, ALİ RIZA; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği; 0000-0003-1790-6987; F-7426-2011
The adaptability of metaheuristics is proliferating rapidly for optimizing engineering designs and structures. The imperative need for the fuel-efficient design of vehicles with lightweight structures is also a soaring demand raised by the different industries. This research contributes to both areas by using both the hybrid Taguchi salp swarm algorithm-Nelder-Mead (HTSSA-NM) and the manta ray foraging optimization (MRFO) algorithm to optimize the structure and shape of the automobile brake pedal. The results of HTSSA-NM and MRFO are compared with some well-established metaheuristics such as horse herd optimization algorithm, black widow optimization algorithm, squirrel search algorithm, and Harris Hawks optimization algorithm to verify its performance. It is observed that HTSSA-NM is robust and superior in terms of optimizing shape with the least mass of the engineering structures. Also, HTSSA-NM realize the best value for the present problem compared to the rest of the optimizer.
Reptile search algorithm and kriging surrogate model for structural design optimization with natural frequency constraints
(Walter De Gruyter Gmbh, 2022-10-26) Bureerat, Sujin; Panagant, Natee; Mehta, Pranav; Yıldız, Ali Rıza; Yıldız, Betül Sultan; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.; 0000-0002-7493-2068; AAL-9234-2020
This study explores the use of a recent metaheuristic algorithm called a reptile search algorithm (RSA) to handle engineering design optimization problems. It is the first application of the RSA to engineering design problems in literature. The RSA optimizer is first applied to the design of a bolted rim, which is constrained optimization. The developed algorithm is then used to solve the optimization problem of a vehicle suspension arm, which aims to solve the weight reduction under natural frequency constraints. As function evaluations are achieved by finite element analysis, the Kriging surrogate model is integrated into the RSA algorithm. It is revealed that the optimum result gives a 13% weight reduction compared to the original structure. This study shows that RSA is an efficient metaheuristic as other metaheuristics such as the mayfly optimization algorithm, battle royale optimization algorithm, multi-level cross-entropy optimizer, and red fox optimization algorithm.