Differential Evolution Algorithm Implementation

In this text, the author presents their self-developed differential evolution algorithm and expresses pride in this accomplishment. To enhance reader understanding, we provide detailed explanations of differential evolution principles including mutation, crossover, and selection operations. The implementation demonstrates key components such as population initialization strategy using random sampling, differential mutation with scaling factor control, binomial crossover operations with probability parameters, and greedy selection mechanisms. We include practical examples showing parameter optimization applications and performance comparisons. Additionally, we discuss potential development directions including hybrid approaches combining DE with local search techniques, and future application domains like large-scale optimization and multi-objective problems. Resources including code structure diagrams and parameter tuning guidelines are provided to support further study and implementation.