Fundamental PSO Algorithm and Three Classic Improved Variants

Particle Swarm Optimization (PSO) is an intelligent optimization algorithm that simulates the social behavior of bird flocks or fish schools, widely applied in function optimization and neural network training. Its core concept involves adjusting particle positions through individual and collective experience to seek optimal solutions.

Basic PSO Program Flow Initialize the particle swarm, including positions, velocities, personal best (pBest), and global best (gBest). Calculate fitness values for each particle and update pBest and gBest. Adjust particle velocities and positions using velocity update formulas: Velocity updates depend on inertia weight, individual cognition, and social learning components. Position updates iterate through current velocities. Repeat iterations until termination conditions are met (e.g., maximum iterations or convergence accuracy).

Three Classic Improved Algorithms GPSO (Generalized PSO) Introduces generalized parameterized velocity update formulas to enhance convergence performance by adjusting weights and acceleration factors. Suitable for high-dimensional complex problems, balancing global exploration and local exploitation capabilities.

ARPSO (Adaptive PSO) Dynamically adjusts inertia weights or learning factors to adaptively optimize search behavior based on particle states. Addresses premature convergence issues, ideal for multimodal function optimization.

HPSO (Hybrid PSO) Integrates mechanisms from other optimization algorithms (e.g., genetic algorithms, simulated annealing) to strengthen global search capabilities. Typical applications include discrete optimization or constraint problems.

Improvement Direction Summary Parameter Adjustment: Such as dynamic inertia weights (ARPSO). Topology Structure: Altering information sharing methods between particles (e.g., neighborhood models). Hybrid Strategies: Incorporating advantages from other algorithms (HPSO).

In practical applications, algorithm variants should be selected based on problem characteristics, with attention to parameter tuning impacts on performance.