DFIG Swarm Optimization Technique

The Differential Evolution (DE) algorithm is a widely adopted swarm optimization technique functioning as a stochastic, population-based evolutionary algorithm. It employs fundamental operations including mutation, crossover, and selection to evolve candidate solutions over successive generations. In practice, DE initializes a population of parameter vectors and iteratively generates new vectors by combining weighted differences between population members with a base vector. The algorithm's mutation strategy typically follows the DE/rand/1 scheme: V = X_r1 + F × (X_r2 - X_r3), where F denotes the scaling factor. Following mutation, binomial crossover creates trial vectors by mixing mutant and target vectors based on a crossover probability (CR). Key implementation aspects involve maintaining population diversity through differential mutations and employing greedy selection to retain superior solutions. Demonstrated to be robust and computationally efficient, DE serves as a standard benchmark for evaluating novel optimization methods. This algorithm constitutes an essential tool for solving diverse optimization problems ranging from engineering design to parameter tuning.