A multi-stage particle swarm for optimum design of truss structures

The contribution of this study is to propose a multi-stage particle swarm optimization (MSPSO) for structural optimization. In this paper, three auxiliary improving mechanisms are added to the standard particle swarm optimization (PSO) in order to enhance its efficiency and reliability dealing with optimum design of truss structures. These mechanisms effectively accelerate the convergence rate of the PSO and also make it robust to attain better optimum solutions during various runs of the algorithm. The effectiveness of the MSPSO is illustrated by several benchmark structural optimization problems. Results demonstrate the efficiency and robustness of the proposed MSPSO algorithm compared to the standard version of the PSO.     

Multi‐expression programming based model for prediction of formation enthalpies of nitro‐energetic materials

There has been considerable interest in predicting the properties of nitro‐energetic materials to improve their performance. Not to mention insightful physical knowledge, computational‐aided molecular studies can expedite the synthesis of novel energetic materials through cost reduction labours and risky experimental tests. In this paper, quantitative structure–property relationship based on multi‐expression programming employed to correlate the formation enthalpies of frequently used nitro‐energetic materials with their molecular properties. The simple yet accurate obtained model is able to correlate the formation enthalpies of nitro‐energetic materials to their molecular structure with the accuracy comparable to experimental precision.     

An evolutionary approach for modeling of shear strength of RC deep beams

In this study, a new variant of genetic programming, namely gene expression programming (GEP) is utilized to predict the shear strength of reinforced concrete (RC) deep beams. A constitutive relationship was obtained correlating the ultimate load with seven mechanical and geometrical parameters. The model was developed using 214 experimental test results obtained from previously published papers. A comparative study was conducted between the results obtained by the proposed model and those of the American Concrete Institute (ACI) and Canadian Standard Association (CSA) models, as well as an Artificial Neural Network (ANN)-based model. A subsequent parametric analysis was carried out and the trends of the results were confirmed via some previous laboratory studies. The results indicate that the GEP model gives precise estimations of the shear strength of RC deep beams. The prediction performance of the model is significantly better than the ACI and CSA models and has a very good agreement with the ANN results. The derived design equation provides a valuable analysis tool accessible to practicing engineers.     

Constrained mean‐variance mapping optimization for truss optimization problems

Truss optimization is a complex structural problem that involves geometric and mechanical constraints. In the present study, constrained mean‐variance mapping optimization (MVMO) algorithms have been introduced for solving truss optimization problems. Single‐solution and population‐based variants of MVMO are coupled with an adaptive exterior penalty scheme to handle geometric and mechanical constraints. These tools are explained and tuned for weight minimization of trusses with 10 to 200 members and up to 1,200 nonlinear constraints. The results are compared with those obtained from the literature and classical genetic algorithm. The results show that a MVMO algorithm has a rapid rate of convergence and its final solution can obviously outperform those of other algorithms described in the literature. The observed results suggest that a constrained MVMO is an attractive tool for engineering‐based optimization, particularly for computationally expensive problems in which the rate of convergence and global convergence are important.