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.

翻译：状态转移算法（STA）是一种用于全局优化的元启发式方法。近期，一种改进型STA——参数最优状态转移算法（POSTA）被提出。POSTA通过参数选择机制优化了扩展算子、旋转算子和轴算子的性能。然而，由于历史信息利用不充分，POSTA在特定问题上仍存在收敛速度慢、求解精度低的问题。为充分利用历史信息，本文将Nelder-Mead（NM）单纯形搜索与二次插值（QI）集成到POSTA中。将该增强型POSTA在20维、30维和50维空间中的14个基准函数上进行测试，并与多种具有竞争力的元启发式方法进行实验对比，验证了所提方法的有效性。