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

To solve optimization problems in continuous space, we propose an adaptive mixed-culture shuffled frog-leaping 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.