Adaptive Particle Swarm Optimization and Its Application Model

In order to accelerate the convergence speed of particle swarm optimization and solve the problem of premature convergence, a diversity activated adaptive particle swarm optimization algorithm is proposed. This algorithm combines the linear descent mechanism of inertia weight and the diversity driven speed strategy, which can better achieve a balance between exploration and development. Numerical experiments have shown that the quality of diversity activated adaptive particle swarm optimization is superior to other algorithms in different dimensions. The experimental results of curve energy smoothing and feature selection show that diversity activated adaptive particle swarm optimization can effectively smooth the curvature changes of B-spline curves, greatly reducing the peak curvature of the smoothed curve. The results demonstrate excellent optimization performance of diversity activated adaptive particle swarm optimization. It not only performs well in benchmark functions, but also achieves significant results in practical applications. The results also validated the efficiency and practicality of diversity activated adaptive particle swarm optimization, and demonstrated its broad application prospects.