Solving Nonlinear Equations Systems with an Enhanced Reinforcement Learning Based Differential Evolution

Nonlinear equations systems (NESs) arise in a wide range of domains. Solving NESs requires the algorithm to locate multiple roots simultaneously. To deal with efficiently, this study presents an enhanced reinforcement learning based differential evolution following major characteristics: (1) design state function uses information on fitness alternation action; (2) different neighborhood sizes and mutation strategies are combined as optional actions; (3) unbalanced assignment method is adopted change reward value select optimal actions. evaluate performance our approach, 30 test problems 18 instances features selected suite. The experimental results indicate that proposed approach can improve solving NESs, outperform several state-of-the-art methods.