Optimization of cryogenic processing parameters based on mathematical test functions using a newer hybrid approach (HAIS-GA)

The article introduces a newer hybrid method (HAIS-GA) of optimizing and choosing the ideal machining parameters for cryogenic processing. It depends on two late methodologies genetic algorithm (GA) and artificial immune system (AIS), which are connected to numerous troublesome combinatorial streamlining issues with specific qualities and shortcomings. These developmental calculations are proposed to find the best arrangement of process factors for the clashing prerequisites in multi objective capacities. Hybrid model optimization also comes with challenges, such as selecting the right combination of techniques, tuning parameters, potential increases in complexity, and the need for expertise in multiple optimization methods. The key reason for this hybrid approach (HAIS-GA) is the improvement in the results that is achieved due to the characteristics of GA and AIS. Three test functions are employed to compare the outcomes in terms of these functions' ability to achieve the lowest value. Cryogenic processing is used to validate the optimised values that were obtained. The attained results showcase that HAIS-GA approach, in conclusion exhibits a more favourable minimal objective function within a reasonable duration. Due to the nature of Unrestricting to local optima, and it being self-adaptive HAIS-GA provides better result compared to GA and AIS. Based on the least value of the objective function and time for each method.