A fast and simple computational model to reach the global optimum in the field of experimental sciences

In the field of experimental science, costly and time consuming experiments are performed, especially in the food engineering. In most of these experiments, interactions between the variables and experimental results are tested to detect the desired experimental results. For these types of experiments, optimization methods are needed to achieve the desired optimal experimental result rapidly. Moreover, these methods should be able to simply apply without computer support. Since the existing complex mathematical optimization methods cannot satisfy these needs, a simple new method was proposed in this study, especially for the food engineering. The proposed method divides the sampling space into 4the number of variables segments in each iteration. Then determines the center of one segment as the coordinates of the global optimum. Moreover, each iteration only requires the (1+(2×the number of variables)) sampling. The proposed method was tested on a sample food engineering experiment previously performed using Response Surface Methodology (RSM). Then, it's superiority and usability was demonstrated comparatively. Finally, a simple and effective optimization method that does not require mathematical operations was proposed.