A simple differential evolution with time-varying strategy for continuous optimization

We propose a novel simple variant of differential evolution (DE) algorithm and call it TVDE because it is a time-varying strategy-based DE algorithm. In our TVDE, three functions with time-varying characteristics are applied to create a new mutation operator and automatically tune the values of two key control parameters (scaling factor and crossover rate) during the evolutionary process. To verify its availability, the proposed TVDE has been tested on the CEC 2014 benchmark sets and four real-life problems and compared to seven state-of-the-art DE variants. The experimental results indicate that the proposed TVDE algorithm obtains the best overall performance among the eight DE algorithms.