Improved ant colony algorithm based on multi-objective optimization control

Aiming how to achieve a high-precision furnace temperature curve in the production process of electronic products, a double-objective optimization model is established, which combines heat conduction and heat convection, and transforms it into a single objective to solve the optimal furnace temperature curve and important parameters through dynamic comprehensive weighting. In this paper, an improved ant colony algorithm has been proposed to address the issue. The population initialization method based on good points is set to make the initial population distribution more uniform. The nonlinear control parameter is utilized to balance the global search and local development ability during different iteration periods. The Markov process is executed to get a wider range of solutions. Compared with other intelligent algorithms, the results show that the improved ant colony algorithm avoids local premature phenomenon, has higher convergence, and the obtained results are more accurate. Using this algorithm to solve the problem can better face the field of precise temperature control.