Browsing by Author "Sait, Sadiq M."
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Publication A novel hybrid marine predators-Nelder-Mead optimization algorithm for the optimal design of engineering problems(Walter, 2021-01-01) Panagant, Natee; Yıldız, Mustafa; Pholdee, Nantiwat; Yıldız, Ali Riza; Bureerat, Sujin; Sait, Sadiq M.; YILDIZ, ALİ RIZA; Yıldız, Mustafa; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü; JTZ-2884-2023; F-7426-2011The marine predators optimization algorithm (MPA) is a recently developed nature-inspired algorithm. In this paper, the Nelder-Mead algorithm is utilized to improve the local exploitation powers of the MPA when described as a hybrid marine predators and Nelder-Mead (HMPANM). Due to the harsh competitive conditions as well as the transition to new vehicles such as hybrid and full-electrical cars, the interest in the design of light and low-cost vehicles is increasing. In this study, a recent metaheuristic addition, a hybrid marine predators optimization algorithm, is used to solve a structural design optimization problem to prove how the HMPANM can be used in solving industrial design problems. The results strongly prove the capability of the HMPANM for the optimum design of components in the automotive industry.Item Comparative investigation of the moth-flame algorithm and whale optimization algorithm for optimal spur gear design(Walter De Gruyter GMBH, 2021-03) Abderazek, Hammoudi; Hamza, Ferhat; Sait, Sadiq M.; Yıldız, Ali Rıza; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği.; 0000-0003-1790-6987; F-7426-2011; 7102365439In this study, two recent algorithms, the whale optimization algorithm and moth-flame optimization, are used to optimize spur gear design. The objective function is the minimization of the total weight of the spur gear pair. Moreover, the optimization problem is subjected to constraints on the main kinematic and geometric conditions as well as to the resistance of the material of the gear system. The comparison between mothflame optimization (MFO), the whale optimization algorithm (WOA), and previous studies indicate that the final results obtained from both algorithms lead to a reduction in gear weight by 1.05 %. MFO and the WOA are compared with four additional swarm algorithms. The experimental results indicate that the algorithms introduced here, in particular MFO, outperform the four other methods when compared in terms of solution quality, robustness, and high success rate.Item Comparision of the political optimization algorithm, the Archimedes optimization algorithm and the Levy flight algorithm for design optimization in industry(Walter De Gruyter GMBH, 2021-04) Pholdee, Nantiwat; Bureerat, Sujin; Kaen, Khon; Sait, Sadiq M.; Yıldız, Betül Sultan; Erdaş, Mehmet Umut; Yıldız, Ali Rıza; Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği.; Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği.; AAL-9234-2020; F-7426-2011; CNV-1200-2022; 57094682600; 57298176600; 7102365439This article focuses on minimizing product costs by using the newly developed political optimization algorithm (POA), the Archimedes 'optimization algorithm (AOA), and the Levy flight algorithm (LFA) in product development processes. Three structural optimization methods, size optimization, shape optimization, and topology optimization, are extensively applied to create inexpensive structures and render designs efficient. Using size, shape, and topology optimization in an integrated way, It is possible to obtain the most efficient structures in industry. The political optimization algorithm (POA) is a metaheuristic algorithm that can be used to solve many optimization problems. This study investigates the search capability and computational efficiency of POA for optimizing vehicle structures. By examining the results obtained, we prove the apparent superiority of the POA to other recent famous metaheuristics such as the Archimedes optimization algorithm and the Levy flight algorithm. The most important result of this paperwill be to provide an impressive aid for industrial companies to fill the gaps in their product design stages.Item Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems(Pergamon-Elsevier Science Ltd, 2021-11-30) Gupta, Shubham; Abderazek, Hammoudi; Mirjalili, Seyedali; Sait, Sadiq M.; Yıldız, Betül Sultan; Yıldız, Ali Rıza; Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği.; AAL-9234-2020; F-7426-2011; 57094682600; 7102365439Determining the solution for real mechanical design problems is a challenging task when using the newly developed and efficient swarm intelligence algorithms. There are so many difficulties to be addressed, including but not limited to mixed decision variables, diverse constraints, inherent errors, conflicting objectives, and numerous locally optimal solutions. This work analyzes the behavior of nine metaheuristic algorithms, namely, salp swarm algorithm (SSA), multi-verse optimizer (MVO), moth-flame optimizer (MFO), atom search optimi-zation (ASO), ecogeography-based optimization (EBO), queuing search algorithm (QSA), equilibrium optimizer (EO), evolutionary strategy (ES) and hybrid self-adaptive orthogonal genetic algorithm (HSOGA). The efficiency of these algorithms is evaluated on eight mechanical design problems using the solution quality and convergence analysis, which verifies the wide applicability of these algorithms to real-world application problems.Item Comparison of recent algorithms for many-objective optimisation of an automotive floor-frame(Inderscience Enterprises, 2019) Panagant, Natee; Pholdee, Nantiwat; Wansasueb, Kittinan; Bureerat, Sujin; Sait, Sadiq M.; Yıldız, Ali Rıza; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği/Konstrüksiyon ve İmalat Bölümü.; F-7426-2011; 7102365439In this paper, an approach called real-code population-based incremental learning hybridised with adaptive differential evolution (RPBILADE) is proposed for solving many-objective automotive floor-frame optimisation problems. Adaptive strategies are developed and integrated into the algorithm. The purpose of these strategies is to select suitable control parameters for each stage of an optimisation run, in order to improve the search performance and consistency of the algorithm. The automotive floor-frame structures are considered as frame structures that can be analysed with finite element analysis. The design variables of the problems include topology, shape, and size. Ten optimisation runs using various optimisers are carried out on two many-objective automotive floor-frame optimisation problems. Twelve additional benchmark tests against all competitors are also performed to demonstrate the search performance of the proposed algorithm. RPBILADE provided better results than other recent optimisers for both the automotive floor-frame optimisation and benchmark problems.Publication Comparison of the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, the salp swarm algorithm for real-world engineering applications(Walter De Gruyter Gmbh, 2021-01-01) Gürses, Dildar; Bureerat, Sujin; Sait, Sadiq M.; Yıldız, Ali Rıza; GÜRSES, DİLDAR; YILDIZ, ALİ RIZA; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü.; JCN-8328-2023 ; F-7426-2011This paper focuses on a comparision of recent algorithms such as the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, and the salp swarm algorithm. The slime mold algorithm (SMA) is a recent optimization algorithm. In order to strengthen its exploitation and exploration abilities, in this paper, a new hybrid slime mold algorithm-simulated annealing algorithm (HSMA-SA) has been applied to structural engineering design problems. As a result of the rules and practices that have become mandatory for fuel emissions by international organizations and governments, there is increasing interest in the design of vehicles with minimized fuel emissions. Many scientific studies have been conducted on the use of metaheuristic methods for the optimum design of vehicle components, especially for reducing vehicle weight. With the inspiration obtained from the above-mentioned methods, the HSMA-SA has been studied to solve the shape optimization of a design case to prove how the HSMA-SA can be used to solve shape optimization problems. The HSMA-SA provides better results as an arithmetic optimization algorithm than the slime mold optimization algorithm, the marine predators algorithm, and the salp swarm algorithm.Item Conceptual comparison of the ecogeography-based algorithm, equilibrium algorithm, marine predators algorithm and slime mold algorithm for optimal product design(Walter De Gruyter GMBH, 2021-07-01) Patel, Vivek; Pholdee, Nantiwat; Sait, Sadiq M.; Bureerat, Sujin; Yıldız, Betül Sultan; Yıldız, Ali Rıza; Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği.; 0000-0003-1790-6987; 0000-0002-7493-2068; AAL-9234-2020; F-7426-2011; 57094682600; 7102365439Vehicle component design is crucial for developing a vehicle prototype, as optimum parts can lead to cost reduction and performance enhancement of the vehicle system. The use of metaheuristics for vehicle component optimization has been commonplace due to several advantages: robustness and simplicity. This paper aims to demonstrate the shape design of a vehicle bracket by using a newly invented metaheuristic. The new optimizer is termed the ecogeography-based optimization algorithm (EBO). This is arguably the first vehicle design application of the new optimizer. The optimization problem is posed while EBO is implemented to solve the problem. It is found that the design results obtained from EBO are better when compared to other optimizers such as the equilibrium optimization algorithm, marine predators algorithm, slime mold algorithm.Item The Harris hawks, grasshopper and multi-verse optimization algorithms for the selection of optimal machining parameters in manufacturing operations(Walter de Gruyter, 2019-08) Sait, Sadiq M.; Li, Xinyu; Yıldız, Betül Sultan; Yıldız, Ali Rıza; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.; 0000-0003-1790-6987; AAL-9234-2020; F-7426-2011; 7102365439; 57094682600In this research, the Harris hawks optimization algorithm (HHO), the grasshopper optimization algorithm (GOA) and the multi-verse optimization algorithm (MVO) have been used in solving manufacturing optimization problems. This paper is the first research study for the optimization of processing parameters for manufacturing processes using the HHO, the GOA, and the MVO in the literature, and in particular, for grinding operations. A well-known grinding optimization problem is solved to prove how effective the HHO, the GOA and the MVO are in solving manufacturing problems and to demonstrate superiority over other algorithms. The results of the HHO, the GOA and the MVO are compared with other methods such as the genetic algorithm, the ant colony algorithm, the scatter search, the differential evolution algorithm, the particle swarm optimization algorithm, simulated annealing, the artificial bee colony, harmony search, improved differential evolution, the hybrid particle swarm algorithm, teaching learning-based optimization algorithms, the cuckoo search, and the fractal search algorithm. The results show that the HHO, the GOA, and the MVO are efficient optimization approaches for obtaining optimal manufacturing variables in manufacturing operations.Publication Hybrid spotted hyena-Nelder-Mead optimization algorithm for selection of optimal machining parameters in grinding operations(Walter De Gruyter Gmbh, 2021-01-01) Pholdee, Nantiwat; Patel, Vivek K.; Sait, Sadiq M.; Bureerat, Sujin; Yıldız, Ali Rıza; YILDIZ, ALİ RIZA; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü.; F-7426-2011In this research, a novel optimization algorithm, which is a hybrid spotted hyena-Nelder-Mead optimization algorithm (HSHO-NM) algorithm, has been introduced in solving grinding optimization problems. A well-known grinding optimization problem is solved to prove the superiority of the HSHO-NM over other algorithms. The results of the HSHO-NM are compared with others. The results show that HSHO-NM is an efficient optimization approach for obtaining the optimal manufacturing variables in grinding operations.Publication Hybrid taguchi-levy flight dis-tribution optimization algorithm for solving real-world design optimization problems(Walter, 2021-06-01) Yıldız, Mustafa; Panagant, Natee; Pholdee, Nantiwat; Bureerat, Sujin; Sait, Sadiq M.; Yıldız, Ali Rıza; Yıldız, Mustafa; YILDIZ, ALİ RIZA; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü; F-7426-2011; JTZ-2884-2023The Levy flight distribution optimization algorithm is a recently developed meta-heuristic. In this study, the Levy flight distribution optimization algorithm and the Taguchi method are hybridized to solve the shape optimization problem, which is the final step in developing optimum structural components. The new method is termed the hybrid Levy flight distribution and Taguchi (HLFD-T) algorithm. Geometric dimensions are used as design variables in the optimization, and the problem is aimed at mass minimization. The constraint in the problem is the maximum stress value. The well-known Kriging meta-modeling approach and a specifically developed hybrid approach have been coupled in this paper to find the component's optimal geometry. The results show that the proposed hybrid algorithm (HLFD-T) has more robust features than the ant lion algorithm, the whale algorithm, and the Levy flight distribution optimization algorithm for obtaining an optimal component geometry.Item Mechanical engineering design optimisation using novel adaptive differential evolution algorithm(Inderscience Enterprises, 2019) Abderazek, Hammoudi; Sait, Sadiq M.; Yıldız, Ali Rıza; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.; F-7426-2011; 7102365439This paper introduces a new adaptive mixed differential evolution (NAMDE) algorithm for mechanical design optimisation problems. The algorithm uses a self-adaptive mechanism to update the values of mutation and crossover factors. Moreover, elitism is used where the best-found individual in each generation is retained. The performance of NAMDE is evaluated by solving 11 well-known constrained mechanical design problems and two industrial applications. Further, comparison results between NAMDE and other recently published methods, for the first problems, clearly illustrate that the proposed approach is an important alternative to solve current real-world optimisation problems. Besides this, new optimal solutions for some engineering problems are obtained and reported in this paper. For the coupling with a bolted rim problem, the objective function improved by 10%. Whereas for the spur minimisation problem, the final design provides a reduction in gearing mass by 7.5% compared to those published in previous works.Item Multi-surrogate-assisted metaheuristics for crashworthiness optimisation(Inderscience, 2019) Aye, Cho Mar; Pholdee, Nantiwat; Bureerat, Sujin; Sait, Sadiq M.; Yıldız, Ali Rıza; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.; F-7426-2011; 7102365439This work proposes a multi-surrogate-assisted optimisation and performance investigation of several newly developed metaheuristics (MHs) for the optimisation of vehicle crashworthiness. The optimisation problem for car crashworthiness is posed to find the shape and size of a crash box while the objective function is to maximise the total energy absorption subject to a mass constraint. Two main numerical experiments are conducted. Firstly, the performance of different surrogate models along with the proposed multi-surrogate model is investigated. Secondly, several MHs are applied to tackle the proposed crashworthiness optimisation problem by employing the best obtained surrogate model. The results reveal that the proposed multi-surrogate model is the best performer. Among the several MHs used in this study, sine cosine algorithm is the best algorithm for the proposed multi-surrogate model. Based on this study, the application of the proposed multi-surrogate model is better than using one particular traditional surrogate model, especially for constrained optimisation.Item A new hybrid Harris hawks-Nelder-Mead optimization algorithm for solving design and manufacturing problems(Walter de Gruyter, 2019-08) Sait, Sadiq M.; Bureerat, Sujin; Pholdee, Nantiwai; Yıldız, Betül Sultan; Yıldız, Ali Rıza; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.; 0000-0001-7592-8733; 0000-0003-1790-6987; F-7426-2011; AAL-9234-2020; AAH-6495-2019; 7102365439; 57094682600In this paper, a novel hybrid optimization algorithm (H-HHONM) which combines the Nelder-Mead local search algorithm with the Harris hawks optimization algorithm is proposed for solving real-world optimization problems. This paper is the first research study in which both the Harris hawks optimization algorithm and the H-HHONM are applied for the optimization of process parameters in milling operations. The H-HHONM is evaluated using well-known benchmark problems such as the three-bar truss problem, cantilever beam problem, and welded beam problem. Finally, a milling manufacturing optimization problem is solved for investigating the performance of the H-HHONM. Additionally, the salp swarm algorithm is used to solve the milling problem. The results of the H-HHONM for design and manufacturing problems solved in this paper are compared with other optimization algorithms presented in the literature such as the ant colony algorithm, genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm, artificial bee colony algorithm, teaching learning-based optimization algorithm, cuckoo search algorithm, multi-verse optimization algorithm, Harris hawks optimization optimization algorithm, gravitational search algorithm, ant lion optimizer, moth-flame optimization algorithm, symbiotic organisms search algorithm, and mine blast algorithm. The results show that H-HHONM is an effective optimization approach for optimizing both design and manufacturing optimization problems.Item A novel chaotic Henry gas solubility optimization algorithm for solving real-world engineering problems(Springer, 2022-06) Pholdee, Nantiwat; Panagant, Natee; Bureerat, Sujin; Sait, Sadiq M.; Yıldız, Betül Sultan; Yıldız, Ali Rıza; Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği.; Uludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği.; 0000-0002-7493-2068; 0000-0003-1790-6987; AAL-9234-2020; F-7426-2011; 57094682600; 7102365439The paper proposes a novel metaheuristic based on integrating chaotic maps into a Henry gas solubility optimization algorithm (HGSO). The new algorithm is named chaotic Henry gas solubility optimization (CHGSO). The hybridization is aimed at enhancement of the convergence rate of the original Henry gas solubility optimizer for solving real-life engineering optimization problems. This hybridization provides a problem-independent optimization algorithm. The CHGSO performance is evaluated using various conventional constrained optimization problems, e.g., a welded beam problem and a cantilever beam problem. The performance of the CHGSO is investigated using both the manufacturing and diaphragm spring design problems taken from the automotive industry. The results obtained from using CHGSO for solving the various constrained test problems are compared with a number of established and newly invented metaheuristics, including an artificial bee colony algorithm, an ant colony algorithm, a cuckoo search algorithm, a salp swarm optimization algorithm, a grasshopper optimization algorithm, a mine blast algorithm, an ant lion optimizer, a gravitational search algorithm, a multi-verse optimizer, a Harris hawks optimization algorithm, and the original Henry gas solubility optimization algorithm. The results indicate that with selecting an appropriate chaotic map, the CHGSO is a robust optimization approach for obtaining the optimal variables in mechanical design and manufacturing optimization problems.Item Optimal design of planetary gear train for automotive transmissions using advanced meta-heuristics(Inderscience Enterprises, 2019) Abderazek, Hammoudi; Sait, Sadiq M.; Yıldız, Ali Rıza; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.; F-7426-2011In this paper, nine recent meta-heuristics have been employed to search for optimal design of an automatic planetary gear train. The function of the designed system is to automatically transmit power and motion in automobiles. Nine mixed decision parameters are considered in the optimisation procedure. The geometric conditions such as the undercutting, the maximum overall diameter of the transmission, as well as the spacing of multiple planets are taken into account to ensure an optimum design. All the above algorithms are tested both quantitatively and qualitatively for solution quality, robustness, and their time complexity is determined. Results obtained illustrate that the utilised approaches can effectively solve the planetary gearbox problem. Besides this, the comparative study indicates that roulette wheel selection-elitist differential evolution (ReDE) outperforms the other algorithms in terms of the statistical results, and FA has the best convergence behaviour. Meanwhile, multi-verse optimisation (MVO) and butterfly optimisation algorithm (BOA) performed better than the other used algorithms when computation time was considered.Publication Optimization of constrained mechanical design problems using the equilibrium optimization algorithm(Walter, 2021-06-01) Abderazek, Hammoudi; Yıldız, Ali Rıza; Sait, Sadiq M.; YILDIZ, ALİ RIZA; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomot Mühendisliği Bölümü; 0000-0003-1790-6987; F-7426-2011In this work, the optimization of structural and mechanical problems is carried out using the equilibrium optimizer (EO), which is a recent physical-based algorithm.The the ten-bar planar truss structure, planetary gearbox, hydrostatic thrust bearing, and robot gripper mechanism problems are solved using the EO algorithm. The results achieved using the EO in solving these problems are compared with those of recent algorithms. The computational results show that EO yields better outcomes and competitive results that can also be applied for the other problems studied.Item Optimum design of an air suspension seat using recent structural optimization techniques(Walter De Gruyter Gmbh, 2020-03) Bureerat, Sujin; Sait, Sadiq M.; Yıldız, Ali Rıza; Balkan, Ayhan; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.; 0000-0003-1790-6987; CFA-2110-2022; F-7426-2011; 57215817460; 7102365439This article is on the optimum design of driver seats in commercial vehicles, which are expected to provide high comfort to drivers during long travel distances. This comfort is usually achieved through a pneumatic actuation and suspension motion that provides alignment with the road. Moreover, the seat which directly hosts the driver is supposed to ensure safety and best working conditions. The result is that seat weight increases considerably when all comfort, safety, and reliability features are incorporated. But, weight is an important factor in automotive transportation as it results in increased cost and also undesired emission values. For this reason, even the smallest reduction in the weight of commercial vehicles can lead to high amounts of savings. In this study, weight reduction and structural strengthening are targeted together with the help of topography optimization. Optimizations were made, and in these studies, the desired lightness and target strength levels were achieved. As a result of the study, optimization, weight reduction, strengthening, product performance, production cost and material cost outputs were considered and remodeled for better quality. An ECE R14 seat belt pulling test was simulated as an ultimate solution, and the expected results were obtained yet again. With the help of optimization and simulation tools, the appropriateness of the outcomes was assessed at the end of the study and found to achieve a 7 % reduction in weight and 13 mm displacement improvement.