Orthogonal Design-Based Control Vector Parameterization Combined With Improved Seagull Optimization Algorithm for Dynamic Optimization Problems

This paper proposes an orthogonal design-based control vector parameterization (OCVP, for short) and a Gaussian distribution-based seagull optimization algorithm (GSOA) for dynamic optimization problems (DOPs). OCVP uses orthogonal experimental design to analyze the dynamic model to capture the fluctuation characteristics of the optimal control trajectory. Then using the ranges obtained in the orthogonal experiment to guide the construction of the time grid. Based on the seagull optimization algorithm (SOA), GSOA introduces the initialization idea based on Gaussian distribution and the dimension-order mutation operator based on Gaussian distribution. The initialization idea cleverly uses Gaussian distribution to generate the initial population that conforms to the chemical process. The mutation operator uses the dimension-order mutation method to improve the optimization performance of SOA. OCVP and GSOA are combined to form a new optimization method, named OCVP-GSOA. In the application of four typical chemical DOPs, the simulation results show that OCVP-GSOA can achieve similar or even higher solution accuracy. Furthermore, OCVP and control vector parameterization are compared, and GSOA and other meta-heuristic algorithms are compared. The results show that OCVP can achieve higher solution accuracy in most cases, and GSOA can achieve better performance.