A hybrid method based on krill herd and quantum-behaved particle swarm optimization

A novel hybrid Krill herd (KH) and quantum-behaved particle swarm optimization (QPSO), called KH–QPSO, is presented for benchmark and engineering optimization. QPSO is intended for enhancing the ability of the local search and increasing the individual diversity in the population. KH–QPSO is capable of avoiding the premature convergence and eventually finding the function minimum; especially, KH–QPSO can make all the individuals proceed to the true global optimum without introducing additional operators to the basic KH and QPSO algorithms. To verify its performance, various experiments are carried out on an array of test problems as well as an engineering case. Based on the results, we can easily infer that the hybrid KH–QPSO is more efficient than other optimization methods for solving standard test problems and engineering optimization problems.     

Formulation of soil angle of shearing resistance using a hybrid GP and OLS method

In the present study, a prediction model was derived for the effective angle of shearing resistance (?′) of soils using a novel hybrid method coupling genetic programming (GP) and orthogonal least squares algorithm (OLS). The proposed nonlinear model relates ?′ to the basic soil physical properties. A comprehensive experimental database of consolidated-drained triaxial tests was used to develop the model. Traditional GP and least square regression analyses were performed to benchmark the GP/OLS model against classical approaches. Validity of the model was verified using a part of laboratory data that were not involved in the calibration process. The statistical measures of correlation coefficient, root mean squared error, and mean absolute percent error were used to evaluate the performance of the models. Sensitivity and parametric analyses were conducted and discussed. The GP/OLS-based formula precisely estimates the ?′ values for a number of soil samples. The proposed model provides a better prediction performance than the traditional GP and regression models.     

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.