Differential Evolution-Based Particle Swarm Optimization Algorithm

The Differential Evolution-Based Particle Swarm Optimization algorithm, also known as the Global Best Particle Swarm Optimization algorithm, is an optimization algorithm designed for solving complex optimization problems. This hybrid approach combines the strengths of Differential Evolution (DE) and Particle Swarm Optimization (PSO) to enhance global search capabilities. Differential Evolution introduces diversity through differential operations (typically involving mutation and crossover operations) that help explore the solution space more effectively. Particle Swarm Optimization simulates the social behavior of bird flocks, where particles update their positions based on personal and global best experiences. By integrating these two methodologies, the algorithm achieves more comprehensive exploration of the solution space, leading to superior optimization results. Implementation typically includes maintaining a population of particles, applying DE operators for mutation and crossover to enhance diversity, and using PSO velocity and position update equations to guide search direction. Consequently, the Differential Evolution-Based Particle Swarm Optimization algorithm demonstrates excellent performance and effectiveness in solving various optimization challenges.