Enhanced Genetic Particle Swarm Optimization Algorithm with Superior High-Dimensional Performance
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The newly enhanced Genetic Particle Swarm Optimization (GPSO) algorithm demonstrates progressively superior performance with increasing dimensional complexity. This hybrid algorithm integrates the global exploration strengths of genetic algorithms with the local exploitation capabilities of particle swarm optimization, effectively addressing high-dimensional optimization challenges while discovering more optimal solutions. Through iterative refinement cycles that incorporate adaptive mutation operators and dynamic inertia weight adjustments, the algorithm exhibits robust search capabilities in complex problem spaces. Key implementation features include chromosome-particle hybrid encoding schemes, fitness-guided selection mechanisms, and velocity-position update rules with boundary handling, ensuring stable convergence and improved solution quality across multidimensional landscapes.
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