An Improved GAFSA Based on Chaos Search and Modified Simplex Method

This paper combines the dynamically adjusting parameters, the chaos search (CS), and the modified simplex method (MS) with GAFSA, and the CS_MS_GAFSA is proposed. The algorithm speeds up the convergence by dynamically adjusting the parameters, and increases the probability of artificial fish escaping local extreme points by chaotic search for the current global optimum value. When the algorithm converges to the global optimum nearby, a simplex is constructed and the algorithm switches to MS which will continue to optimize until a certain stop condition is satisfied. Take the best point of simplex vertex at this time as the optimal value. The computational results on benchmark functions show that CS_MS_GAFSA does improve in optimizing accuracy and convergence speed.