| Abstract |
Computational intelligence refers to an algorithm inspired by the body structure of natural animals and plants and unique landscapes. It can perform simultaneous multithreaded work. In the processing process of the food industry, multiple factors must be considered at the same time to optimize various parameters simultaneously. Therefore, this research combines computational intelligence algorithms with the processing flow of the food industry, conducts research on the optimization of food processing systems and processes, and fully understands the needs of the market, the competitiveness of the company's products, and the people facing marketing. The MA-CI model was used to optimize the entire process of food processing and production, and multiple variables were integrated. The following conclusions were drawn: (1) When the node size = 100, calculate the optimal value of the intelligent model (MA - CI) = 0.3196, the worst value = 0.3120, and the average plus - minus variance = 0.3155 +/- 4.6278 x 10(-6), in each index. Both are better than the Greedy algorithm model and the EA model. When the node size = 200, the optimal value of the calculation intelligence model (MA - CI) = 0.3050, the worst value = 0.2900, and the average plus and minus variance = 0.2970 +/- 8.9251 x 10(-6); when the node size = 300, the calculation intelligence model (MA-CI) optimal value = 0.2608, the worst value = 0.2499, and the mean plus - minus variance = 0.2551 +/- 1.0028 x 10(-5); when the node size = 500, calculate the optimal value of intelligent model (MA - CI)(+) = 0.2857, the worst value = 0.2812, and the mean plus and minus variance = 0.2834 +/- 2.8230 x 10(-6). (2) In the electronic circuit network, calculate the intelligent model (MA-CI): Rc = 0.3174, NMI = 0.7388, Delta Ec = 0, and k = 14.43; in the USAir network, the computational intelligence model (MA-CI) is as follows: Rc = 0.3041, NMI = 0.8632, Delta Ec = 0, and k = 7:43; the computational intelligence model (the uniqueness and optimality of MA-CI) has resulted in the optimal solution for the optimization of the global machining process. (3) Using six online databases to predict and verify the MA-CI model for process optimization in the food industry, it was found that the MA-CI model for process optimization in the food industry was better than the standard value. (4) Perform performance testing on the optimized MA-CI model. It is found that under the MA-CI model, MUTAG = 98.2 +/- 4.3, ENZYMES = 96:2 +/- 4:3, PTC = 85.2 +/- 4.3, PROTEINS = 82.0 +/- 3.2, NCI1 = 80.2 +/- 2.0, and D&D = 91.9 +/- 0.5, which are better than the other models. The optimized MA-CI model can control the profit problem in food processing and production, and it is found that the MA-CI model can make the enterprise obtain the maximum profit in the shortest period. |
