自适应混合文化蛙跳算法求解连续空间优化问题

Adaptive Mixed-culture Shuffled Frog-leaping Algorithm for Continuous-space Optimization

针对连续空间优化问题,提出了一种自适应混合文化蛙跳算法.算法中群体空间采用改进的混合蛙跳算法进行优化,信念空间通过云模型算法对知识进行更新,利用混沌算法和反向学习算法进化外部空间,3种空间通过自适应的接受操作和影响操作来实现知识的交换.最后通过典型复杂函数测试,结果表明该算法具有很好的收敛精度和计算速度,特别适宜于多峰值函数寻优.

To solve optimization problems in continuous space,we propose an adaptive mixed-culture shuffled frogleaping algorithm（ SFLA） in which community space is evolved by the improved SFLA,belief space is updated by the cloud model algorithm,outer space is evolved by the chaos algorithm and an opposition-based learning algorithm,and knowledge about these three spaces is exchanged through adaptive acceptance and effect operations. Finally,based on a typical complex function test,our simulation results indicate that the proposed algorithm has better convergence precision and computing speed and is especially suitable for multimodal function optimization.