  State transition algorithm (STA) is a metaheuristic method for global
optimization. Recently, a modified STA named parameter optimal state transition
algorithm (POSTA) is proposed. In POSTA, the performance of expansion operator,
rotation operator and axesion operator is optimized through a parameter
selection mechanism. But due to the insufficient utilization of historical
information, POSTA still suffers from slow convergence speed and low solution
accuracy on specific problems. To make better use of the historical
information, Nelder-Mead (NM) simplex search and quadratic interpolation (QI)
are integrated into POSTA. The enhanced POSTA is tested against 14 benchmark
functions with 20-D, 30-D and 50-D space. An experimental comparison with
several competitive metaheuristic methods demonstrates the effectiveness of the
proposed method.
