Premature Convergence Issues in Particle Swarm Optimization Iterative Processes

Particle Swarm Optimization (PSO) is a swarm intelligence-based optimization algorithm commonly used to solve optimization problems such as clustering. However, during iterative processes, particle swarms may experience premature convergence, becoming trapped in local optima and preventing the algorithm from discovering global optimal solutions.

To address this issue, two monitoring variables can be implemented in the algorithm to detect the state of each particle and the entire swarm in real-time. Code implementation typically involves tracking particle velocity reduction and population diversity metrics. When premature convergence patterns are detected (e.g., velocity thresholds below minimum limits or diversity indices dropping critically), mutation operations are triggered immediately. These mutation operations, which can include random position resets or velocity perturbations, enhance particle diversity by forcing particles to escape local optima and resume exploration in the multidimensional solution space.

This strategy not only improves the algorithm's exploration capability but also effectively prevents optimization failure due to early swarm convergence. By dynamically adjusting particle behavior through conditional mutation triggers and adaptive parameter tuning, the PSO algorithm can more flexibly adapt to complex optimization problems, ultimately enhancing solution quality. Key functions to implement would include convergence detection modules, diversity calculation methods, and mutation operator functions with configurable parameters.