基于逐层演化的群体智能算法优化

Optimization for swarm intelligence based on layer-by-layer evolution

为能彻底解决群体智能算法早熟问题的同时保持原算法主体不变且可与现有优化理论协同优化,在前期仿真实验和理论证明的基础上,提出了一种逐层演化的改进策略.利用在原算法中构建基于搜索空间压缩理论的自适应系统,通过逐层的压缩、选择、再初始化的操作,以包括压缩后搜索空间在内的社会信息作为遗传知识,指导寻优过程,从而实现最终解精度的提升、避免早熟问题的出现.对基准函数进行仿真实验可以看出该策略在提升算法精度,增强后期个体活性方面具有良好的表现.

A layer-by-layer evolution strategy was proposed to deal with the premature convergence of swarm intelligence as a collaborator with other existing researches based on pre-experiments and simple proofs. For promoting the precision of solution and eviting the premature convergence,the self-adaption system was constructed on the basis of the primal algorithm,operations such as compression,selection and re-initialization using the technology of layer-by-layer,and the social information was used including the compressed research space and the optimal solution. The improvements of precision of solution and the vitality of terminal individuals can be found in results of simulation experiments with benchmark functions.