Distant-relative pointer genetic algorithm-based cross section size optimization method of steel truss structure

Abstract

The invention discloses a distant-relative pointer genetic algorithm-based cross section size optimization method of a steel truss structure, which is used for cross section size optimization design and calculation of the steel truss structure so that the structure conforms to the economical and safe requirements. The method comprises the steps of building a mathematical model of a structural optimization design; mapping a target function and a constraint condition to an adaptive value function so as to further randomly generate an initial population; calculating an adaptive value, and judging whether the adaptive value conforms to a convergence criterion or not; arranging a distant-relative pointer to sequentially complete genetic operator operation and immunity operator operation if the adaptive value does not conform to the convergence criterion, and further completing population substitution; repeatedly calculating the adaptive value until the convergence criterion is satisfied; and outputting an optimization result. The distant-relative pointer can be used for effectively avoiding individual repeated occurrence, meanwhile, the distant-relative pointer has the capability of generating a new mode under the condition of no damage on excellent individual, the global convergence of the algorithm is ensured, and the method is simple to operate; and the local search capability of the algorithm can be improved by vaccination on an immunity operator, individual degeneration can be prevented, and the complete effect of an adaptive technology is ensured.

Description

technical field [0001] The invention belongs to the field of civil engineering, in particular to a method for optimizing the section size of a steel truss structure based on a distant relative pointer genetic algorithm Background technique [0002] The invention applies genetic algorithm to carry out constrained cross-sectional size optimization design for engineering structures, especially steel truss structures, reduces the weight and material cost of the structure, and improves the strength, stiffness and other properties of the structure at the same time, so that the structure meets the requirements of safety and economy. Requirements, has important engineering application value. [0003] With the increase in size and complexity of the actual structure, the standard genetic algorithm often has shortcomings such as premature convergence or low computational efficiency. The two genetic parameters of crossover probability and mutation probability are the key to determine t...

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Problems solved by technology

[0003] With the increase in size and complexity of the actual structure, the standard genetic algorithm often has disadvantages such as premature convergence or low computational efficiency.

The two genetic parameters of crossover probability and mutation probability are the key to determine the performance of the genetic algorithm. Although the academic community has proposed an adaptive technology to make the crossover probability and mutation probability automatically change with the fitness, the actual calculation shows that it is necessary to really improve the convergence speed of the algorithm. , the traditional adaptive technology is still lacking

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