Improved Particle Swarm Optimization And Its Application To Structural Optimum Design

Structural optimization design not only reduce the weight and cost of structure, but also optimize the improvement of structural strength, stiffness, stability, reliability, vibration characteristics,etc. so it is a modern design and computational mechanics of important research direction.With the rapid development of mathematical theory and computer technology ,traditional optimization methods, in many respects, can not meet the needs of modern computing.The emergence of swarm intelligence algorithm can solve many new problems in engineering optimization, these algorithms search mechanism on behalf of the population and evolutionary technology, the algorithm parameters and operating decisions determine its search behavior, it has strong robustness, stability and global and local search capabilities, suitable for any function, so as to solve some shortcomings of traditional optimization methods. PSO (Particle swarm optimization, PSO algorithm)is a swarm intelligence algorithms in a very typical algorithm, which originated from migratory birds gathered in the course of the simulation.It is characterized mainly fast convergence, robustness, parameter adjustment small and easy to implement, it has been used in many engineering fields.Owing to it is a advanced optimization method ,it can better solve discrete issues and some complex non-convex nonlinear problem.Based on the standard particle swarm optimization, this search algorithm for optimization of capacity and optimal design for solving structural engineering analysis and improvement issues.This paper mainly contains the following main parts:(1) First introduces the structural optimization theory and optimization methods of development, details the principles of particle swarm optimization and development, moreover gives the calculation of the structural optimization process.(2) Introduces some improved particle swarm optimization and application process, including ideas based on dynasties and discrete particle swarm optimization Particle swarm optimization.(3) This paper presents an improved particle swarm optimization PSOBF (Particle Swarm Optimization Based On Bacterial Foraging), diversity-based method of control strategies, can increase the rejection and to avoid premature convergence, so as to achieve local and global convergence balance.(4) By the improved PSO algorithm for solving structural engineering design applications, including optimization of reinforced concrete beams, the optimization framework,trusses discrete optimization.Through several practical engineering analysis results compared with the existing references, this improved particle swarm algorithm become more economical and reasonable data to showing that the algorithm is feasible and effective.