Ardeshir Bahreininejad

Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems

A novel population-based algorithm based on the mine bomb explosion concept, called the mine blast algorithm (MBA), is applied to the constrained optimization and engineering design problems. A comprehensive comparative study has been carried out to show the performance of the MBA over other recognized optimizers in terms of computational effort (measured as the number of function evaluations) and function value (accuracy). Sixteen constrained benchmark and engineering design problems have been solved and the obtained results were compared with other well-known optimizers. The obtained results demonstrate that, the proposed MBA requires less number of function evaluations and in most cases gives better results compared to other considered algorithms.

Damage detection of truss bridge joints using Artificial Neural Networks

Recent developments in Artificial Neural Networks (ANNs) have opened up new possibilities in the domain of inverse problems. For inverse problems like structural identification of large structures (such as bridges) where in situ measured data are expected to be imprecise and often incomplete, ANNs may hold greater promise. This study presents a method for estimating the damage intensities of joints for truss bridge structures using a back-propagation based neural network. The technique that was employed to overcome the issues associated with many unknown parameters in a large structural system is the substructural identification. The natural frequencies and mode shapes were used as input parameters to the neural network for damage identification, particularly for the case with incomplete measurements of the mode shapes. Numerical example analyses on truss bridges are presented to demonstrate the accuracy and efficiency of the proposed method.

A context-aware adaptive learning system using agents

Evolution of Web technologies has made e-learning a popular common way of education and training. As an outcome, learning content adaptation has been the subject of many research projects lately. This paper suggests a framework for building an adaptive Learning Management System (LMS). The proposed architecture is based upon multi-agent systems and uses both Sharable Content Object Reference Model (SCORM) 2004 and semantic Web ontology for learning content storage, sequencing and adaptation. This system has been implemented upon a well known open-source LMS and its functionalities are demonstrated through the simulation of a scenario mimicing the real life conditions. The result reveals the system effectiveness for which it appears that the proposed approach may be very promising.

Water cycle algorithm for solving multi-objective optimization problems

In this paper, the water cycle algorithm (WCA), a recently developed metaheuristic method is proposed for solving multi-objective optimization problems (MOPs). The fundamental concept of the WCA is inspired by the observation of water cycle process, and movement of rivers and streams to the sea in the real world. Several benchmark functions have been used to evaluate the performance of the WCA optimizer for the MOPs. The obtained optimization results based on the considered test functions and comparisons with other well-known methods illustrate and clarify the robustness and efficiency of the WCA and its exploratory capability for solving the MOPs.

Optimum gradient material for a functionally graded dental implant using metaheuristic algorithms

Despite dental implantation being a great success, one of the key issues facing it is a mismatch of mechanical properties between engineered and native biomaterials, which makes osseointegration and bone remodeling problematical. Functionally graded material (FGM) has been proposed as a potential upgrade to some conventional implant materials such as titanium for selection in prosthetic dentistry. The idea of an FGM dental implant is that the property would vary in a certain pattern to match the biomechanical characteristics required at different regions in the hosting bone. However, matching the properties does not necessarily guarantee the best osseointegration and bone remodeling. Little existing research has been reported on developing an optimal design of an FGM dental implant for promoting long-term success. Based upon remodeling results, metaheuristic algorithms such as the genetic algorithms (GAs) and simulated annealing (SA) have been adopted to develop a multi-objective optimal design for FGM implantation design. The results are compared with those in literature.

Optimization of laminate stacking sequence for minimizing weight and cost using elitist ant system optimization

This paper presents the application of ant colony optimization (ACO) for the multi-objective optimization of hybrid laminates for obtaining minimum weight and cost. The investigated laminate is made of glass-epoxy and graphite-epoxy plies to combine the lightness and economical attributes of the first with the high-stiffness property of the second using a modified variation of ACO so called the elitist ant system (EAS) in order to make the tradeoff between the cost and weight as the objective functions. First natural frequency was considered as a constraint. The obtained results using the EAS method including the Pareto set, optimum stacking sequences, and the number of plies made of either glass or graphite fibers were compared with those using the genetic algorithm (GA) and any colony system (ACS) reported in literature. The comparisons confirm the advantage of hybridization and showed that the EAS algorithm outperformed the GA and ACS in terms of functions value and constraint accuracy.

Parallel training of neural networks for finite element mesh decomposition

Abstract This paper describes a parallel processing implementation for neural computing and its application to finite element mesh decomposition. The parallelized neural network software developed is based on the public domain NASA developed program NETS 2.01, which is based on the back propagation algorithm of Rumelhart et al . [Learning internal representation by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Edited by D. E. Rummelhart and J. L. McClelland), Vol. 1: Foundations . MIT Press, MA (1986)]. The principal focus of this research concerns the parallel implementation. Comparisons between sequential and parallel versions are given. Finally a structural design problem concerned with finite element mesh generation is solved using the parallel neural network software.

Finite element mesh partitioning using neural networks

This paper examines the application of neural networks to the partitioning of unstructured adaptive meshes for parallel explicit time-stepping finite element analysis. The use of the mean field annealing (MFA) technique, which is based on the mean field theory (MFT), for finding approximate solutions to the partitioning of the finite element meshes is investigated. The partitioning is based on the recursive bisection approach. The method of mapping the mesh bisection problem onto the neural network, the solution quality and the convergence times are presented. All computational studies were carried out using a single T800 transputer.

Two parameter-tuned meta-heuristics for a discounted inventory control problem in a fuzzy environment

HighlightsA nearly real-world MP-MP inventory control problem under discounts and budget constraints is investigated.The required storages space is considered a fuzzy number.The goal is to find the optimal ordered quantities of the products.A HSA is developed to solve the complex problem. In this paper, a nearly real-world multi-product, multi-period inventory control problem under budget constraint is investigated, where shortages in combination with backorders and lost sales are considered for each product. The ordered quantities of products are delivered in batch sizes with a known number of boxes, each containing a pre-specified number of products. Some products are purchased under an all unit discount policy, and others are purchased under an incremental quantity discount with fuzzy discount rates. The goal is to find the optimal ordered quantities of products such that not only the total inventory cost but also the required storage space (considered as a fuzzy number) to store the products is minimized. The weighted linear sum of objectives is applied to generate a single-objective model for the bi-objective problem at hand and a harmony search algorithm is developed to solve the complex inventory problem. As no benchmarks are available to validate the obtained results, a particle-swarm optimization algorithm is employed to solve the problem in addition to validate the results given by the harmony search method. The parameters of both algorithms are tuned using both Taguchi and response surface methodology (RSM). Finally, to assess the performance of the proposed algorithms some numerical examples are generated, and the results are compared statistically.

On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model

Advanced automatic data acquisition is now widely adopted in manufacturing industries and it is common to monitor several correlated quality variables simultaneously. Most of multivariate quality control charts are effective in detecting out-of-control signals based upon an overall statistics in multivariate manufacturing processes. The main problem of such charts is that they can detect an out-of-control event but do not directly determine which variable or group of variables has caused the out-of-control signal and what is the magnitude of out of control. This study presents a hybrid learning-based model for on-line analysis of out-of-control signals in multivariate manufacturing processes. This model consists of two modules. In the first module using a support vector machine-classifier, type of unnatural pattern can be recognized. Then by using three neural networks for shift mean, trend and cycle it can be recognized magnitude of mean shift, slope of trend and cycle amplitude for each variable simultaneously in the second module. The performance of the proposed approach has been evaluated using two examples. The output generated by trained hybrid model is strongly correlated with the corresponding actual target value for each quality characteristic. The main contributions of this work are recognizing the type of unnatural pattern and classification major parameters for shift, trend and cycle and for each variable simultaneously by proposed hybrid model.