In this study, the entire weight, joint displacements and load-carrying capacity of tubular lattice girders are simultaneously optimized by a multi-objective optimization algorithm, named Non-dominated Sorting Genetic Algorithm II (NSGAII). Thus, the structural responses of tubular lattice girders are obtained by use of arc-length method as a geometrically nonlinear analysis approach and utilized to check their member strengths at each load step according to the provisions of the American Petroleum Institute specification (API RP2A-LRFD 1993). In order to improve the computing capacity of proposed optimization approach, while the optimization algorithm is hybridized with a radial basis neural network approach, an automatic lattice girder generator is included into the design stage. The improved optimization algorithm, called ImpNSGAII, is applied to both a benchmark lattice girder with 17 members and a lattice girder with varying span lengths and loading conditions. Consequently, it is demonstrated: 1) the optimal lattice girder configuration generated has a higher load-carrying capacity ensuring lower weight and joint displacement values; 2) the use of a multi-objective optimization approach increases the correctness degree in evaluation of optimality quality due to the possibility of performing a trade-off analysis for optimal designations; 3) the computing performance of ImpNSGAII is higher than NSGAII’s.

No abstract

No abstract

A new genetic algorithm (GA) methodology, Bipopulation-Based Genetic Algorithm with Enhanced Interval Search (BGAwEIS), is introduced and used to optimize the design of truss structures with various complexities. The results of BGAwEIS are compared with those obtained by the sequential genetic algorithm (SGA) utilizing a single population, a multipopulation-based genetic algorithm (MPGA) proposed for this study and other existing approaches presented in literature. This study has two goals: outlining BGAwEIS's fundamentals and evaluating the performances of BGAwEIS and MPGA. Consequently, it is demonstrated that MPGA shows a better performance than SGA taking advantage of multiple populations, but BGAwEIS explores promising solution regions more efficiently than MPGA by exploiting the feasible solutions. The performance of BGAwEIS is confirmed by better quality degree of its optimal designations compared to algorithms proposed here and described in literature.

Both the entire weight and joint displacements of grid structures are minimized at the same time in this study. Four multiobjective optimization algorithms, NSGAII, SPEAII, PESAII, and AbYSS are employed to perform computational procedures related to optimization processes. The design constraints related to serviceability and ultimate strength of grid structure are implemented from Load and Resistance Factor Design-American Institute of Steel Constructions (LRFD-AISC Ver.13). Hence, while the computational performances of these four optimization algorithms are compared using different combinations of optimizerrelated parameters, the various strengths of grid members are also evaluated. For this purpose, multiobjective optimization algorithms (MOAs) employed are applied to the design optimization of three application examples and achieved to generate various optimal designations using different combinations of optimizer-related parameters. According to assessment of these optimal designations considering various quality indicators, IGD, HV, and spread, AbYSSS shows a better performance comparatively to the other three proposed MOAs, NSGAII, SPEAII, and PESAII.