Mohammad Reza Ghasemi

Engineering optimization based on ideal gas molecular movement algorithm

The present work introduces a new metaheuristic optimization method based on the ideal gas molecular movement (IGMM) to solve mathematical and engineering optimization problems. Ideal gas molecules scatter throughout the confined environment quickly. This is embedded in the high speed of molecules, collisions between them and with the surrounding barriers. In IGMM algorithm, the initial population of gas molecules is randomly generated and the governing equations related to the velocity of gas molecules and collisions between those are utilized to accomplish the optimal solutions. To verify the performance of the IGMM algorithm, some mathematical and engineering benchmark optimization problems, commonly used in the literature, are inspected. Comparison of results obtained by IGMM with other optimization algorithms show that the proposed method has a challenging capacity in finding the optimal solutions and exhibits significance both in terms of the accuracy and reduction on the number of function evaluations vital in reaching the global optimum.

Ranked-Based Sensitivity Analysis for Size Optimization of Structures

This article proposes a novel ranked-based method for size optimization of structures. This method uses violation-based sensitivity analysis and borderline adaptive sliding technique (ViS-BLAST) on the margin of feasible nonfeasible (FNF) design space. ViS-BLAST maybe considered a multiphase optimization technique, where in the first phase, the first arbitrary local optimum is found by few analyses and in the second phase, a sequence of local optimum points is found through jumps and BLASTs until the global optimum is found. In fact, this technique reaches a sensitive margin zone where the global optimum is located, with a small number of analyses, utilizing a space-degradation strategy (SDS). This strategy substantially degrades the high order searching space and then proceeds with the proposed ViS-BLAST search for the optimum design. Its robustness and effectiveness are then defied by some well-known benchmark examples. The ViS-BLAST not only speeds up the optimization procedure but also it ensures nonviolated optimum designs.

A fast multi-objective optimization using an efficient ideal gas molecular movement algorithm

Recently, the ideal gas molecular movement (IGMM) algorithm was proposed by the authors as a new metaheuristic optimization technique for solving SOPs. In this paper, the intention is to extend the IGMM to solve MOPs while some modifications to the algorithm are taken place. The major improvement to the algorithm comprises usage of a neighbor-based non-dominated selection technique and defining a set of non-dominated solutions stored in an archive causing a globally faster convergence of the procedure. To evaluate the proposed algorithm, a set of standard benchmark problems, the so-called ZDT functions and two engineering benchmarks, are solved and the results were compared with five known multi-objective algorithms provided in the literature. Three different performance metrics; generational distance, spacing and maximum spread are introduced as well to evaluate multi-objective optimization problems. The Wilcoxon’s rank-sum nonparametric statistical test was also attempted which resulted on the fact that the proposed algorithm may exhibit a significantly better performance than those other techniques. The results from the real engineering applications also prove the advancement of the MO-IGMM performance in practice. Compared to five other multi-objective optimization evolutionary algorithms, simulation results show that in most cases, the proposed MO-IGMM is capable to find a much better uniformly spread of solutions with a faster convergence to the true Pareto optimal front.

Enhanced IGMM optimization algorithm based on vibration for numerical and engineering problems

Recently, a new optimization procedure called ideal gas molecular movement (IGMM) algorithm was introduced by the authors. The algorithm is based on the movement of ideal gas molecules in an isolated medium. A close-up track of their movements reveals the fact that the gas molecules have locally motions such as vibration and rotation in addition to their translational motions. In this paper, a modified version of IGMM is introduced by simulating the vibration of each molecule in the form of an operand referred to as molecular operand on vibrational effect (MOVE) which causes a significant escalation in the convergence speed specially at the early stages of the optimization process. A parametric study has also been carried out with regard to alteration of four most effective parameters of the VIGMM algorithm. Consequently, the best ranges of alteration for each parameter are proposed. The results of applying the proposed vibration-based IGMM (VIGMM) to different numerical and engineering benchmark functions and the Wilcoxon’s rank-sum nonparametric statistical test intensely show that the MOVE operand significantly boosts the performance of the IGMM and one could certify the significance of VIGMM, proposed in the present study, over some other meta-heuristic optimization algorithms.

Seismic performance and damage incurred by monolithic concrete self-centering rocking walls under the effect of axial stress ratio

Self-centering rocking walls offer the possibility of minimizing repair costs and downtimes, and also nullify the residual drift after seismic events, thanks to their self-centering properties. In this paper, the effect of axial stress ratio on the behavior of monolithic self-centering rocking walls is investigated by utilizing a developed finite element model. To verify the validity of the finite element model, results and observed damage in the model are compared with those of a full-scale wall test. The axial stress ratio is varied from 0.024 to 0.30 while keeping the other structural parameters constant. For qualitative damage evaluation, the observed damage in the model compared with expected damage states of desired performance levels. In order to evaluate the incurred damage quantitatively, the amount of crushing and damage in the wall is calculated by utilizing several ratios (crushing ratio and damage ratio). Furthermore, seismic response factors (i.e., μ, R and Cd) are calculated for different axial stress ratio values. The obtained results showed that, in order to satisfy the requirements of desired performance levels, the maximum axial stress ratio should be approximately within the range of 0.10–0.15. In addition, the maximum overall damage ratio and crushing ratio are suggested to be less than 5%. For axial stress ratio higher than 0.15, the flag-shaped pattern of hysteresis curves completely disappeared and the variation of displacement ductility is less sensitive to axial stress ratio. Considering the maximum axial stress ratio limited to 0.150, values of 4 and 3.5 are conservatively proposed as a period-independent response modification factor and displacement modification factor of the investigated structural wall, respectively.

Probability-Based Damage Detection of Structures Using Surrogate Model and Enhanced Ideal Gas Molecular Movement Algorithm

Generally, updating a finite element model can be considered as an optimization problem where its physical parameters may be adjusted such that analytically computed features, using the updated FE model, are consistent with those obtained from experimentally. The objective function can be defined as a sum of squared difference between analytically computed and experimentally measured data. To meet this goal in this paper therefore, for efficiently reducing the computational cost of the model during the optimization process of damage detection, the structural response is evaluated using properly a trained surrogate model. Surrogate models have received increasing attention for use in detecting damage of structures based on vibration modal parameters. However, uncertainties existing in the measured vibration data may lead to false or unreliable output results from such model. Here, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states.

An Element-Free Galerkin-Based Multi-objective Optimization of Laminated Composite Plates

An element-free Galerkin method is presented to analyze isotropic and laminated composite plates. This method employs the moving least square technique to approximate functions. In the analysis procedure, a collocation method is used to enforce boundary conditions. A consistent multi-objective optimization procedure is also applied. The function introduced here consists of minimizing the weight and cost, as well as of maximizing the load. A genetic algorithm is used for the optimization process.

Comparative studies of metamodeling and AI-Based techniques in damage detection of structures

Abstract Despite advances in computer capacity, the enormous computational cost of running complex engineering simulations makes it impractical to rely exclusively on simulation for the purpose of structural health monitoring. To cut down the cost, surrogate models, also known as metamodels, are constructed and then used in place of the actual simulation models. In this study, structural damage detection is performed using two approaches. In both cases ten popular metamodeling techniques including Back-Propagation Neural Networks (BPNN), Least Square Support Vector Machines (LS-SVMs), Adaptive Neural-Fuzzy Inference System (ANFIS), Radial Basis Function Neural network (RBFN), Large Margin Nearest Neighbors (LMNN), Extreme Learning Machine (ELM), Gaussian Process (GP), Multivariate Adaptive Regression Spline (MARS), Random Forests and Kriging are used and the comparative results are presented. In the first approach, by considering dynamic behavior of a structure as input variables, ten metamodels are constructed, trained and tested to detect the location and severity of damage in civil structures. The variation of running time, mean square error (MSE), number of training and testing data, and other indices for measuring the accuracy in the prediction are defined and calculated in order to inspect advantages as well as the shortcomings of each algorithm. The results indicate that Kriging and LS-SVM models have better performance in predicting the location/severity of damage compared with other methods. In the second approach, after locating precisely the eventual damage of a structure using modal strain energy based index (MSEBI), to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, the MSEBI of structural elements is evaluated using a properly trained surrogate model. The results indicate that after determining the damage location, the proposed solution method for damage severity detection leads to significant reduction of computational time compared to finite element method. Furthermore, engaging colliding bodies optimization algorithm (CBO) by efficient surrogate model of finite element (FE) model, maintains the acceptable accuracy of damage severity detection.

Modified Ideal Gas Molecular Movement Algorithm Based on Quantum Behavior

Recently, the ideal gas molecular movement (IGMM) algorithm was proposed by the authors as a new metaheuristic optimization technique for solving single and multi-objective optimization problems. Ideal gas molecules scatter throughout the confined environment quickly. This is embedded in the high speed of molecules, collisions between them and with the surrounding barriers. In IGMM algorithm, the initial population of gas molecules is randomly generated and the governing equations related to the velocity of gas molecules and collisions between those are utilized to accomplish the optimal solutions. In this paper a modified IGMM algorithm is proposed based on quantum theory. Quantum based IGMM (QIGMM) is intended for enhancing the ability of the local search and increasing the individual diversity in the population. QIGMM improve capability of IGMM in avoiding the premature convergence and eventually finding the function optimum. startlingly, all these are obtained without introducing additional operators to the basic IGMM algorithm. The effectiveness of these improvements is tested by some standard benchmark optimization problems. experimental results show that, QIGMM algorithm is more effective and efficient than the original IGMM and other approaches.

New neural network-based response surface method for reliability analysis of structures

Abstract In this paper, a new algorithm is introduced for reliability analysis of structures using response surface method based on a group method of data handling-type neural networks with general structure (GS-GMDH-type NN). A multilayer network of quadratic neurons, GMDH, offers an effective solution to modeling nonlinear systems without an explicit limit state function. In the proposed method, the response surface function is determined using GMDH-type neural networks. This is then connected to a reliability method, such as first-order or second-order reliability methods (FORM or SORM) or Monte Carlo simulation method to predict the failure probability (Pf). In the proposed method, the use of the GMDH-type neural network with general structure, where all neurons from previous layers are used to produce neurons in the new layer, can improve the limit state function. In addition, the structure of the neural network and its weight are simultaneously optimized by genetic algorithm and singular value decomposition. As a result, the obtained model has no significant error, despite its simplicity. Moreover, the obtained limit state function is explicit and allows direct use of FORM and SORM methods. To determine the accuracy and efficiency of the proposed method, four numerical examples are solved and their results are compared to other conventional methods. The results show that the proposed method is simply applicable to analyzing the reliability of large complex and sophisticated structures without an explicit limit state function. The proposed approach is a high accurate method that can significantly reduce computing time compared with direct Monte Carlo method.