Improved PSO algorithm based on variant sigmoid function and particle variation

The invention provides a PSO (particle swarm optimization) improved algorithm based on a variant sigmoid function and particle variation. The PSO improved algorithm comprises the following steps: step 1, setting a target function for STVPSO optimization and parameters needing to be optimized; step 2, setting PSO parameters; 3, initializing particle positions and speeds; step 4, calculating a particle objective function value; 5, updating historical optimal positions of the particles; 6, updating a global historical optimal position; 7, judging whether the maximum number of iterations is reached or not; 8, updating the inertia weight; step 9, variation of the optimal particle position of the population; step 10, variation of the worst particle position of the population; step 11, updating the speed and the position of the particle; 12, circulating the steps 4-11; step 13, outputting a global optimal historical position; by applying the technical scheme, the global optimization capability and the local optimiza