A study on hybrid random signal-based learning and its applications

Abstract

In this paper, we propose three effective hybrid random signal-based learning (RSL) algorithms which are a combination of RSL with simulated annealing (SA) and a genetic algorithm (GA) to obtain a global solution that can be used in combinatorial optimization problems. GAs are becoming more popular because of their relative simplicity and robustness. GAs are global search techniques for non-linear optimization, but they are not good at fine-tuning solutions. RSL is similar to the reinforcement learning of neural networks using random signals. It can find an accurate solution in local search space. However, it is poor at hill-climbing, whereas simulated annealing has the ability to perform probabilistic hill-climbing. Therefore, combining them yields effective hybrid algorithms, i.e. hybrid RSL algorithms, with the merits of both. To check the generalization ability of the proposed algorithms, the optimizations of several benchmark test functions are considered, while the optimization of a fuzzy logic controller for the inverted pendulum is detailed to show the applicability of the proposed algorithms to fuzzy control.

Acknowledgements

The authors would like to thank the referees and the Editor for their valuable comments and suggestions, which greatly enhanced the clarity of the paper.