Optimization Parameters Of Cottonseed Pressing Test Based On GSO

GSO is a new swarm intelligence optimization algorithm，the algorithm simulatesthe luminescence behavior nature glowworm swarm foraging and courtship，by comparingthe intensity to achieve the purpose of exchange．Using in civil engineering effect of slopecreep model，put forward a kind of squeezing the model to simulate the deformation in theprocess of oil crops in squeezing，oil and other process， after research in cottonseedcrushing application prove the validity of the model．GSO optimizing creep model parameters in the process of crushing solution is aglowworm swarm optimization algorithm is a typical application．For possible problems，GSO，including the iteration step size too large oscillationoccurred near the optimal solution， the firefly individuals may find less than goodneighbors and stop searching，etc．，with the improved algorithm to solve to individualmobile． By making less individual fireflies in good moving randomly finding newindividuals to improve the efficiency of optimization，through comparing the neighborhoodaverage distance to set moving step length，enhanceing the precision of solution．Theimproved algorithm is applied to the parameter optimization problem，obtaining basicsatisfactory results．Aiming at the problem of single algorithm，，using the combination of particle swarmand fireflies group evolved a hybrid method，it makes the particle swarm optimizationneighborhood of the individuals with fireflies ability， enhances the ability of globaloptimization，the advantages of both the particle swarm and fireflies group，the resultsshow that both global and local optimization ability are very well．...