带子群的自组织蠕虫算法在优化方面的应用

Subgroup-Self-Organizing Worm Algorithm in Function Optimization

带子群的自组织蠕虫算法(Subgroup-Self-OrganizingWormAlgorithm,SSOMA)是一种全新的基于涌现方法的多模态优化算法。与传统的多模态算法相比,该算法具有计算简单、收敛性好、精度高且不需要任何先验知识等优点。对该算法在高维多模态问题优化方面的应用进行了一定的探索,提出了适用于高维函数的算法,用经典测试函数对该算法进行了仿真实验,并进行了计算复杂度分析,结果表明该算法在高维多模态函数优化方面具有较为理想的应用前景。

In this paper Subgroup-Self-Organizing Worm Algorithm（SSOMA） is presented based on the emergence method in complexity research and the classical searching methods,The main idea of this algorithm carl be described as follow：search the neighboring regions of peak points through the prophase optlmization;select a small quantity of units to build up subgroup .in every region,and process the anaphase optimization in these subgroups,by which the peak points will be found in these subgroups,The computation complexity can be lowered obviously and the convergence rate can also he improved efficiently by this method,At last,experiments are given to solve several typical.