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.
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, a newly developed moth-flame optimization algorithm (MFO) is presented for solving optimization problems in manufacturing industry. A well-known milling optimization problem is solved to emphasize the effectiveness of the MFO in the optimization of manufacturing problems. In the optimization problem solved in this paper, the main aim is to maximize the profit rate for multi-tool milling operations considering difficult constraints. The results demonstrate that the MFO is an effective optimization method for the solution of manufacturing optimization problems.
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.
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