PID Parameters Optimization Of Hydraulic Straightener Based On Hybrid Swarm Intelligence Algorithm

With the development of science and technology,the market competition is intensifying day by day,low cost and high output are the key to improve the competitiveness of the enterprises,which need to be optimized for every aspect in actual production.The hybrid swarm intelligence algorithm can integrate all the advantages of different algorithms,and has solved a lot of optimization problems in engineering practice.Therefore,Particle Swarm Optimization(PSO)algorithm,Bat Algorithm(BA),Ant Colony Optimization(ACO)algorithm,hybrid algorithm and its application is researched for this paper.First of all,aiming at PSO algorithm can not truthfully simulate the complex biological behavior of particles during the search process,therefore,the PSO algorithm is improved from two aspects which are force rules and dynamic topology structure that can truly reflect the information interaction about particles,so the Two-phase PSO based on Directed Self-organising Dynamic Topology(TPSO-DSDT)is proposed.The proposed algorithm is compared with other improved PSO algorithms,which confirmed the feasibility of the proposed algorithm.Secondly,aiming at the speed and position updating method of BA is too single that can not simulate the essence of bats that are willing to interact with better but not worse in actual nature for the search process,becase of the similarity between BA and PSO algorithm,the improved method of PSO is transplanted into the BA,in other words,it also improved from two aspects which are force rules and dynamic topology structure,so the Dynamic Topology Repulsive Force Bat Algorithm(DTRFBA)is proposed.The proposed algorithm is compared with other improved BA algorithms,which confirmed the feasibility of the proposed algorithm.Moreover,aiming at ACO algorithm is depended on pheromone excessively in selecting path process,this single pheromone updating mechanism is defective in finding the global optimal solution and is easy to make the algorithm fall into a local optimum,in order to simulate the behavior of ants in actual nature,the Multi-stage Adaptive Pheromone Ant Colony Optimization(MAPACO)algorithm is proposed.The performance of the proposed algorithm is tested by using the Traveling Salesman Problem(TSP),which confirmed the feasibility of the proposed algorithm.Finally,aiming at the optimization and application defects of single population intelligence algorithm because of the limitation of single biological population,considering the advantages of PSO algorithm,BA,and ACO algorithm,the three improved algorithms are mixed in different stages,so a Hybrid Swarm Intelligence Algorithm(HSIA)is proposed.HSIA is used to solve the PID control optimization of hydraulic leveler and the problem of hydraulic manifold processing shop scheduling,the optimization results are compared with representative PSO algorithm and BA,which confirmed the feasibility of the proposed algorithm.