The Application Of Intelligent Optimization Algorithm In Mechanical Design

As a new optimization technique intelligent optimization algorithm has efficient performance and wide adaptability,including genetic algorithm,particle swarm optimization,simulated annealing and tabu search method,artificial neural network,These algorithms can be more adapt to the solving needs of multi-situations than traditional optimization methods,in the field of computer science,combinatorial optimization,engineering optimization,intelligent optimization algorithm has a wide prospects.In this thesis,Based on the theory study of genetic algorithm and particle swarm optimization,the function optimization problem was solved by using this two kinds of intelligent algorithm.Simulation results of testing functions and the advantages and disadvantages of the two algorithms was analysis,a new intelligent optimization algorithms was put forward.The new intelligent optimization was test by test function and validated by mechanical optimization examples.The results showed that the proposed algorithm theory and practice was feasible and effect of optimization was obvious.Firstly,mathematical model,constituent elements,operational procedures and operational processes was described based on deep theory study of genetic algorithm and particle swarm optimization,which parameters were selected,convergence was analyzed,and then the advantages and disadvantages were summarized,and eventually determine the convergence area of the particle swarm algorithm.Secondly,simulation was conducted for the five kinds of testing functions with genetic algorithm and particle swarm optimization algorithm,the result shows that the optimal value can be get with two kind of intelligent optimization algorithms,but there are some drawbacks,such as unstable iterations,long run time,lack of local optimum value.Genetic particle swarm optimization algorithm was presented,which combined crossover with selection step and dominated by particle swarm optimization algorithm,to simulate the five kinds of testing functions,the results shows that the new optimization process is more smooth and running time is shorter,optimization is more effective,so the proposed new algorithm is effective and feasible.Finally,optimize volume of traction machine worm gear and the governor spring to obtain the result that the new optimization algorithm,genetic algorithm combined with particle swarm optimization,is the most effective,at the same time,compare the result with traditional optimization methods,the validation and feasibility of new optimization algorithm was verified in mechanical design.