Improved seagull optimization algorithm combined with random division method to solve dynamic optimization problems

Abstract

The numerical solution of dynamic optimization problem is often sought for chemical processes, but the discretization of control variables is a difficult problem. Therefore, we propose improved seagull optimization algorithm (ISOA) combined with random division method to solve dynamic optimization problems. Firstly, based on the analysis of the seagull optimization algorithm, this paper introduces the cognitive part in the process of a seagull’s attack behavior to make the group approach the best position. Secondly, this paper uses the 14 benchmark test functions to verify ISOA. Finally, the improved seagull optimization algorithm is combined with the random division method to solve two chemical dynamic optimization problems. The experimental results show that ISOA algorithm has better performance in function optimization.