Hybrid Strategy Enhanced Particle Swarm Optimization Algorithm

This hybrid strategy-enhanced Particle Swarm Optimization algorithm comprises two primary source files: the main execution script (hpso.m) and parameter configuration module (hPSOoptions.m). Key algorithmic improvements are manifested in three aspects:

1. Implementation of novel hybrid strategies that substantially boost PSO performance through dynamic parameter adaptation and multi-phase optimization techniques, with core logic embedded in hpso.m's iteration loop structure

2. Modular code architecture with clear separation of concerns - hPSOoptions.m handles parameter initialization including cognitive/social factors and inertia weights, while hpso.m manages particle velocity updates and position tracking through matrix operations

3. Extensive experimental validation confirms the algorithm's superior performance metrics compared to SPSO, particularly in convergence speed and solution accuracy across benchmark functions

These enhancements render the algorithm more effective and reliable for complex optimization problems. Researchers can leverage the well-documented parameter interface in hPSOoptions.m and modular functions in hpso.m for experimental customization and further algorithmic refinements.

In summary, this hybrid strategy-based PSO algorithm provides a robust framework for optimization challenges, with strong potential for real-world applications in engineering design, machine learning parameter tuning, and computational intelligence systems.