Improved Seagull Optimization Algorithm Combined with an Unequal Division Method to Solve Dynamic Optimization Problems

Abstract

The numerical solution of the dynamic optimization problem is often sought for chemical processes, but the discretization of control variables is a difficult problem. 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, the algorithm adds the mechanism of natural selection, where the fitness value is used to sort the population, and the best half is used to replace the worst half, so as to find out the optimal solution. Finally, the improved seagull optimization algorithm (ISOA) is combined with the unequal division method to solve dynamic optimization problems. The feasibility of the method is verified by three practical examples of dynamic optimization in chemical industry.