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In this paper, the sine-cosine optimization algorithm (SCO) is used to solve the shape optimization of a vehicle clutch lever. The design problem is posed for the shape optimization of a clutch lever with a mass objective function and a stress constraint. Actual function evaluations are based on finite element analysis, while the response surface method is used to obtain the equations for objective and constraint functions. Recent optimization techniques such as the salp swarm algorithm, grasshopper optimization algorithm, and sine-cosine algorithm are used for shape optimization. The results show the ability of the sine-cosine optimization algorithm to optimize automobile components in the industry. King Fahd University of Petroleum and Minerals Khon Kaen University

The shape optimization of mechanical and automotive component plays a crucial role in the development of automotive technology. Presently, the use of derivative-free metaheuristics in combination with finite element analysis for mechanical component design is one of the most focused on topics due to its simplicity and effectiveness. In this research paper, the mine blast algorithm (MBA) is used to solve the problem of shape optimization for a vehicle door hinge to prove how the MBA can be used for solving shape optimization problems in designing electrical vehicles. The results show the advantage of the MBA for designing optimal components in the automotive industry.

Due to harsh competitive conditions and the transition to new vehicles such as hybrid and full-electrical, the interest in the design of light and low-cost vehicles is increasing. In this paper, a recent metaheuristic procedure which is an equilibrium optimization algorithm (EOA) is used to solve a structural design optimization problem for a vehicle seat bracket to prove how the EOA can be used in solving industrial design problems. This paper is the first application of the EAO to real-world problems in the literature. The results strongly prove the capability of the EOA for designing optimum components in the automotive industry. Isringhausen-Aunde Teknik King Fahd University of Petroleum and Minerals Khon Kaen University

This article presents an implementation of one of the latest optimization methods of obtaining light vehicle designs. First, the problem of coupling with a bolted rim is optimized using the butterfly optimization algorithm (BOA). Finally, the BOA is used to solve the shape optimization of a vehicle suspension arm. It is utilized from the Kriging metamodeling method to obtain equations of objective and constraint functions in shape optimization. At the end of the research effort in this paper, the weight reduction of the suspension arm by using the BOA is 32.9%. The results show the BOA's ability to design better optimum components in the automotive industry. King Fahd University of Petroleum and Minerals Khon Kaen University

There 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.

As a result of the requirements imposed by international organizations and governments on fuel emissions, there is a growing interest in the design of lightweight vehicles with low-fuel emissions. Metaheuristic methods have been widely used for the optimum design of vehicle components in recent years for which successful results have been reported. Encouraged by such results obtained from the methods mentioned, the Henry gas solubility optimization algorithm (HGSO), a recently developed optimization method, is used to solve the shape optimization of a vehicle brake pedal to prove how HGSO can be used for solving shape optimization problems. This paper is the first application of the HGSO in connection with real-world optimization problems in the literature. The results show HGSO's ability to design better optimal components in the automotive industry. Pandit Deendayal Petroleum University King Fahd University of Petroleum and Minerals

The product design process has a very important effect on product costs. It aims to develop products that are able to compete by achieving an optimum design in the product design process. This research presents the first application in the literature of the grey wolf, whale, water cycle, ant lion and sine-cosine optimization algorithms for the optimum design of vehicle components. In this study, the optimal structural model of a vehicle connecting rod was determined. In the optimization process, various design alternatives were created by using the latin hypercube method. Stress analysis was performed for each of these designs. According to the generated responses, equations for objective and constraint functions were obtained. The optimization problem was solved using the above mentioned algorithms which have been newly developed in the literature, resulting in optimum connection rod design. The results demonstrate that the grey wolf, whale, water cycle, ant lion and sine-cosine algorithms are very important options in optimizing design and manufacturing optimization problems.

In 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. Huazhong University of Science and Technology King Fahd University of Petroleum and Minerals

In 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.

In the present study, the fatigue behaviour of a clutch diaphragm spring is investigated experimentally and numerically. Differential evolution optimisation algorithm and response surface methodology are used to define optimal variables of the diaphragm springs under constraints both minimum stress and required clamp load. The required clamp load is checked by the chi-square theorem vs. to target clamp load curve. Ten design variables are considered for creating a design of experiment with two steps, including finger shape optimisation with eight variables, load, and stress optimisation with two variables as the aim of maximum fatigue definition. One hundred seventy-five different designs are analysed numerically. As a result of the optimisation study, the optimum design is provided an endless lifetime subject to the required load, minimum stress, and minimum weight. The optimum design is manufactured and tested experimentally. Valeo