Truss optimization on shape and sizing with frequency constraints based on genetic algorithm

Abstract

Truss optimization on shape and sizing with frequency constraints are highly nonlinear dynamic optimization problems. Coupling of two different types of design variables, nodal coordinates and cross-sectional areas, often lead to divergence while multiple frequency constraints often cause difficult dynamic sensitivity analysis. So optimal criteria method and mathematical programming, which need complex dynamic sensitivity and are easily trapped into the local optima, are difficult to solve the problems. To solve the truss shape and sizing optimization simply and effectively, a Niche Hybrid Genetic Algorithm (NHGA) is proposed. The objective of NHGA is to enhance the exploitation capacities while preventing the premature convergence simultaneously based on the new hybrid architecture. Niche techniques and adaptive parameter adjustment are used to maintain population diversity for preventing the premature convergence while simplex search is used to enhance the local search capacities of GAs. The proposed algorithm effectively alleviates premature convergence and improves weak exploitation capacities of GAs. Several typical truss optimization examples are employed to demonstrate the validity, availability and reliability of NHGA for solving shape and sizing optimization of trusses with multiple frequency constraints.

Chaotic biogeography algorithm for size and shape optimization of truss structures with frequency constraints

“…The sensitivity analyses are carried out on the CBBO From Tab. 2, in Case 1 , it can be concluded that CBBO gives lightest design as compared to the results obtained by Grandhi and Venkayya [2], Sedaghati and et al [3], Wang et al [5] and Lingyun et al [10], but slightly heavier design than DPSO [12] method. Also it is clear that the values of mean weight and standard deviation for CBBO are relatively less than other methods.…”

“…5, the results obtained by the CBBO and BBO are compared with those reported in the literature like NHGA [10], PSO [11], CSS, enhanced CSS [29] and DPSO [12]. From this table, it can be observed that CBBO significantly outperforms other methods in terms of the values of best, mean and standard deviation, which shows its stability in reaching the optimal weight of the structure during 20 independent runs.…”

“…From Tab. 9, it can be concluded that CBBO gives small mean weight as compared to CSS [29], Enhanced CSS [29], CSS-BBBC [13] PSO [11], NHGA [10] and Lin et al [34], but slight large mean weight when compared with the DPSO [12] method. However, it should be noted that the CBBO produces much smaller overall standard deviation than all other methods.…”

“…Lingyun et al [10] introduced hybrid Niche Hybrid Genetic Algorithm (NHGA) to shape and size optimization of truss structures with frequency constraints. In this method, the exploitation capacities of GA are enhanced while the diversity of population is maintained.…”

Optimización estructural y topológica de estructuras morfológicamente no definidas mediante algoritmos genéticos

“…Lingyun et al [240] presentaron un Algoritmo Genético sometido a restricciones de frecuencia, aplicándolo a la estructura de diez barras, un puente estudiado previamente por Wang y Arora [406] y una cúpula de 52 barras.…”