The Study Of Optimization Design For Rigidity Four-linkage Portal Crane Luffing System

Designers are always trying to make their design products the most compact,material most provinces, the lowest cost under the premise of meeting performancerequirements. Therefore, more and more mechanical optimization designs areapplied to the design of mechanical products. And there are a large number of gantrycranes in ports, which are most often used in ports. The gantry crane has complexstructure and maximum of mechanisms in handling machinery. The design of gantrycrane luffing system is difficult and there is no good solution to this problem. Studyon optimization method of portal crane luffing system design has theoreticalsignificance and engineering application value.Boom optimization model and counterweight balance optimization model aremodeled. Genetic algorithm and particle swarm algorithm are used to solve themodels. And this paper uses Visual C++programming. Basic genetic algorithm andstandard particle swarm optimization is easy to fall into local optimal solution. Andthey swings around near optimal solution when close to the optimal solution, whichleads to the problem of slow convergence. Therefore, the improved algorithm isnecessary. In later iterations when using genetic algorithm, increasing mutationprobability and reducing crossover probability will strengthen the capacity of localsearch. This improvement strategy increases the accuracy of the algorithm forsolving. The improvement strategy of using hierarchical genetic algorithm reducesthe possible values of the optimization variables, thereby reducing the probability offalling into a local solution.Dynamic inertia weight particle swarm optimization can change the inertiaweight according to whether the global optimum individual updates or not. Thisstrategy can changes reasonably the algorithm global search ability and local searchability, which not only increases the probability of obtaining the optimal solution,but also accelerates the computing speed. The two algorithms solve the optimizationproblem from the optimization results.This paper develops a piece of software for luffing system optimization design, which provides a simple and friendly user interface for the designer.