一种基于自适应动态惯性权重加压缩因子的人工藻算法

Artificial Algae Algorithm Based on Self-Adaptive Dynamic Inertia Weight and Compression Factor

针对标准的人工藻算法(AAA)会由于参数选取不当等原因导致过早收敛和易陷入局部最优解等问题,本文提出一种自适应动态惯性权重(SW)加压缩因子(CF)的人工藻算法(CFSWAAA).为了平衡算法的全局探索和局部改良能力,自适应动态惯性权重被引入到人工藻算法中:为了控制和约束人工藻位置的移动距离,压缩因子被引入到人工藻算法的位置更新中,从而提高算法的收敛速度.最后文章利用4个标准测试函数对改进的算法进行了仿真测试.仿真结果表明,基于自适应动态惯性权重加压缩因子的人工藻算法相比现有的其他四种算法具有较高的优化性能.

Artificial algae algorithm( AAA) may convergence early or stuck into local optimization due to improper parameters selection, artificial algae algorithm based on self-adaptive dynamic inertia weight( SW) and compression factor( CF) was proposed. To balance global search and local search,self-adaptive dynamic inertia weight( SW) was introduced to artificial algae algorithm( AAA). The compression factor was introduced into the location update formula of AAA to control the move distance of location of artificial algae,and thus improved the convergence speed of the algorithm. Four benchmark functions were employed to test results compared with the algorithms of other four algorithms.