Application of Hybrid PSO and SQP Algorithm in Optimization of the Retardance of Citrate Coated Ferrofluids

The citrate (citric acid, CA) coated ferrofluids with great magneto-optical retardance can meet the high magnetic responsive demand, especially in widely potential biomedical applications such as hyperthermia and magnetic resonance imaging. In this study, the measured retardances are based on the Taguchi method with nine tests for four parameters, including pH of suspension, molar ratio of CA to Fe3O4, CA volume, and coating temperature. The retardance obtained from the double centrifugation test is also included. Three optimization algorithms including the particle swarm optimization (PSO), the sequential quadratic programming (SQP), and a hybrid PSO-SQP algorithm are executed to obtain high retardance. The comparisons are made among the retardance results obtained from these algorithms. Seven start points chosen from the orthogonal test are input into the SQP, the PSO is applied to the stepwise regression equation, and while executing the hybrid PSO-SQP algorithm, the parametric combination obtained by the PSO is adopted as the start point in the SQP simulation. The global optimum retardance and the corresponding parameter values are effectively assured by the global search ability of the PSO and the local search ability of the SQP.