基于文化混洗蛙跳算法求解连续空间优化问题

针对混洗蛙跳算法在求解高维函数时易陷入局部最优解的问题, 提出一种文化混洗蛙跳算法, 利用群体空间和信念空间的个体通过接受函数和影响函数完成 信息交换和全局寻优. 首先, 信念空间个体通过螺旋更新和随机游走的方式在较优个体附近寻找更优个体; 其次, 群体空间的最差个体通过借鉴不同知识平衡局部寻优与全局探索的关系, 进而提高算法的寻优精度并加快收敛速度; 最后, 将该算法与12种智能算法进行寻优对比, 对典型高维基准函数的测试结果表明, 该算法的收敛精度和计算速度均较好.

Cultural Shuffled Frog Leaping Algorithm for Continuous Space Optimization Problem

Aiming at the problem that the cultural shuffled frog leaping algorithm was easy to fall into local optimal solution when solving high-dimensional functions, we proposed a cultural shuffled frog leaping algorithm, which used the individuals in group space and belief space to complete information exchange and global optimization through reception function and influence function. Firstly, belief space individuals searched for better individuals around the superior individuals by spiral updating and random walk. Secondly, the worst individuals in group space balanced the relationship between local optimization and global exploration by learning from different knowledge, so as to improve the accuracy of optimization and speed up convergence of the algorithm. Finally, compared the proposed algorithm with 12 intelligent algorithms, the test results of typical high-dimensional benchmark function show that the algorithm has good convergence accuracy and calculation speed.