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Adaptive Particle Swarm Optimization algorithm introduces entropy and average particle distance concepts to standard PSO, significantly improving convergence speed while reducing local optimum entrapment, making it more effective for solving complex optimization problems. Implementation typically involves dynamic inertia weight adjustments and diversity maintenance mechanisms through entropy-based calculations.
The Adaptive Particle Swarm Optimization algorithm improves upon standard PSO by incorporating entropy and average particle distance concepts, significantly accelerating convergence while maintaining global search capabilities. This enhancement reduces susceptibility to local optima and effectively handles complex optimization problems through dynamic parameter adjustments and swarm diversity monitoring.