Showing 3 results for Space Truss
A. Kaveh, M. Kalateh-Ahani, M.s. Masoudi,
Volume 1, Issue 2 (6-2011)
Abstract
Evolution Strategies (ES) are a class of Evolutionary Algorithms based on Gaussian mutation and deterministic selection. Gaussian mutation captures pair-wise dependencies between the variables through a covariance matrix. Covariance Matrix Adaptation (CMA) is a method to update this covariance matrix. In this paper, the CMA-ES, which has found many applications in solving continuous optimization problems, is employed for size optimization of steel space trusses. Design examples reveal competitive performance of the algorithm compared to the other advanced metaheuristics.
A. Hadidi, A. Kaveh, B. Farahmand Azar, S. Talatahari, C. Farahmandpour,
Volume 1, Issue 3 (9-2011)
Abstract
In this paper, an efficient optimization algorithm is proposed based on Particle Swarm Optimization (PSO) and Simulated Annealing (SA) to optimize truss structures. The proposed algorithm utilizes the PSO for finding high fitness regions in the search space and the SA is used to perform further investigation in these regions. This strategy helps to use of information obtained by swarm in an optimal manner and to direct the agents toward the best regions, resulting in possible reduction of the number of particles. To show the computational advantages of the new PSO-SA method, some benchmark numerical examples are studied. The PSO-SA algorithm converges to better or at least the same solutions, while the number of structural analyses is significantly reduced
S. Sarjamei, M. Sajjad Massoudi, M. Esfandi Sarafraz,
Volume 12, Issue 1 (1-2022)
Abstract
The damage identification of truss constructions was investigated in this work. Damage detection is defined through an inverse optimization problem. A function defined as a combination of mode shapes and natural frequencies is examined to minimize damage structures. This guided approach considerably reduces the computational cost and increases the accuracy of optimization. This index mostly exhibits an acceptable performance. Gold Rush Optimization (GRO), an artificial intelligence system based on the power of human thinking and decision-making, was employed to address damage detection. The programming was done in MATLAB. Validation and verification were carried out using a 10, 25, 200, 272, and 582 bar truss. A comparison between the GRO, MCSS, PSO and TLBO is conducted to show the efficiency of the GRO in finding the global optimum. The results show that utilizing the proposed function and the GRO optimization technique to discover truss damaged structure in the quickest time possible is both reliable and stable.
