精英反向与二次插值改进的黏菌算法

针对基本黏菌算法(slime mould algorithm,SMA)易陷入局部最优值、收敛精度较低和收敛速度较慢的问题,提出精英反向学习与二次插值改进的黏菌算法(improved slime mould algorithm,ISMA).精英反向学习策略有利于提高黏菌种群多样性和种群质量,提升算法全局寻优性能与收敛精度;利用二次插值生成新的黏菌个体,并用适应度评估更新全局最优解,有利于增强算法局部开发能力,减少算法收敛时间,使算法跳出局部极值.通过求解多个单模态、多模态和高维度测试函数进行不同算法之间的对比,结果显示,结合两种策略的ISMA具有较高的寻优精度、寻优速度和鲁棒性.

Elite opposition-based learning quadratic interpolation slime mould algorithm

In order to solve the problems of easily fall into the local optimal value, low convergence accuracy and slow convergence speed in the basic slime mould algorithm(SMA), this paper put forward an improved slime mould algorithm(ISMA) based on elite opposition-based learning and quadratic interpolation. Elite opposition-based learning strategy was conducive to improve the diversity and quality of population, improved the global optimization performance and convergence accuracy of the algorithm. It used quadratic interpolation to generate new individuals and fitness evaluation to update the global optimal solution was propitious to enhance the local development ability, reduced the convergence time and made the algorithm jump out of the local extremum. By solving multiple unimodal, multi- modal and high-dimensional test functions, the results show that the ISMA combined with the two strategies has higher optimization accuracy, optimization speed as well as robustness.