In this study, TEACHING-LEARNING-BASED OPTIMIZATION ((TLBO)) ALGORITHM is employed for the first time for OPTIMIZATION of real world truss bridges. The objective function considered is the weight of the structure subjected to design constraints including internal stress within bar elements and serviceability (deflection). Two examples demonstrate the effectiveness of (TLBO) ALGORITHM in OPTIMIZATION of such structures. Various design groups have been considered for each problem and the results are compared. Both tensile and compressive stresses are taken into account. The results show that (TLBO) has a great intrinsic capability in problems involving nonlinear design criteria.

Increasing the profits and reducing the risks have always been of the most important issues of concern to the investors in the financial markets. In recent years, many solutions and proposals have been suggested in respect to the frequency of portfolio OPTIMIZATION issue, with the highest return and the lowest possible risk. One of the most prominent suggestions is the Markowitz Model which is mostly known as the Modern Portfolio Theory. On the other hand, the (TLBO) ALGORITHM which has been presented in 2010 is one of the most efficient meta-heuristic methods to solve the OPTIMIZATION problem.In this study, we are attempting to solve the portfolio OPTIMIZATION problem, according to the framework of the model introduced by Markowitz and using (TLBO) ALGORITHM. For this purpose, the data related to the returns of 20 companies listed in TSE during the period 2012-2016 were collected. It is worth mentioning that four criteria including variance, mean absolute deviation, semi-variance and conditional value at risk (CvaR) were used in order to measure the risk level in this investigation.

In this paper, recommended spiral passive micromixer was designed and simulated. spiral design has the potential to create and strengthen the centrifugal force and the secondary flow. A series of simulations were carried out to evaluate the effects of channel width, channel depth, the gap between loops, and flowrate on the micromixer performance. These features impact the contact area of the two fluids and ultimately lead to an increment in the quality of the mixture. In this study, for the flow rate of 25 μl/min and molecular diffusion coefficient of 1×10-10 m2/s, mixing efficiency of more than 90% is achieved after 30 (approximately one-third of the total channel length). Finally, the optimized design fabricated using proposed 3D printing method.

The complicated problem of truss shape and size OPTIMIZATION with multiple frequency constraints is investigated in this paper. A recently developed metaheuristics called teachinglearning- BASED OPTIMIZATION ((TLBO)) ALGORITHM is used for the first time to solve this kind of problem. Contrary to other metaheuristics, the procedure of (TLBO) is simple to implement since no tuning parameters need to be adjusted. Analyses of structures are performed by a finite element code in MATLAB which is used in conjunction with an OPTIMIZATION code BASED on (TLBO). Various benchmark problems are solved with this technique and the results are compared with those found by other methods including metaheuristics such as PSO, HS and FA. In all test cases, the results show that (TLBO) leads to very satisfactory results i.e. lighter structures which satisfy all frequency constraints. The results of this study indicate excellent inherent capacity of the approach in dealing with complicated dynamic non-linear OPTIMIZATION problems.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is BASED on a basic genetic ALGORITHM, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and BASED on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic ALGORITHM and multi-objective OPTIMIZATION goals. The genetic ALGORITHM's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the ALGORITHM suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-BASED strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence ALGORITHM BASED on the binary ALGORITHM of particle swarm OPTIMIZATION and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories BASED on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm OPTIMIZATION ALGORITHM to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed ALGORITHM compared to other ALGORITHMs presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

Introduction:  As one of the most essential needs of living beings, clean air quality has been threatened by natural and human activities. In recent years, dust storms have been increasing spatially and temporally, causing numerous damages to social, economic, and environmental health for the residents of the southern and southwestern regions of Iran. In the present study, MODIS sensor data were used to investigate dust storms and detect horizontal optical depth. Materials and Methods: The  advantages of MODIS sensor data include high spectral and temporal resolution. Additionally, meteorological station data were collected BASED on the study period. After preprocessing the data and preparing field observations, the necessary features for modeling were extracted using the differential method between selected bands of each MODIS sensor image, along with features extracted from ground-BASED meteorological station sensors. After further investigations and evaluations and using the viewpoints of meteorological experts, 36 differential features from various MODIS image bands and six features from ground-BASED meteorological station data, totaling 42 features, were extracted. Subsequently, using feature selection techniques, the best features were identified. A novel method named ML-BASED GMDH, which improves the GMDH neural network by altering partial functions with machine learning models, was employed to detect dust concentration and horizontal optical depth. To achieve optimal accuracy, the hyper-parameters of this model were heuristically tuned using the (TLBO) OPTIMIZATION ALGORITHM. Additionally, machine learning methods such as Basic GMDH, SVM, MLP, MLR, RF, and their ensemble models were implemented to compare with the main approach. According to the results, the (TLBO)-tuned ML-BASED GMDH method provided superior accuracy in detecting dust concentration compared to the aforementioned machine-learning methods. Results and Discussion: The SVM-PSO method was selected as the best method in the feature selection phase, the RF method was chosen as the best method among basic classification methods, and the Ensemble SVM and Ensemble RF methods were selected as the best methods in the ensemble and classification phase. It was also observed that using the ensemble approach led to a desirable improvement in horizontal optical depth classification. In the second approach, a method titled ML-BASED GMDH, which improves the GMDH neural network by altering partial functions with machine learning ALGORITHMs, was used for estimating dust concentration. Additionally, to achieve suitable accuracy, the hyper-parameters of this model were finely tuned using the (TLBO) OPTIMIZATION ALGORITHM. The results showed that this method provided appropriate accuracy in estimating dust concentration and horizontal optical depth, out performing the best-selected methods from the first approach

In this paper a new approach using TEACHING-LEARNING-BASED OPTIMIZATION ((TLBO)) is presented for the placement of Distributed Generators (DGs) in radial distribution systems in south of Kerman. In this approach a multiple objective planning framework is used to evaluate the impact of DG placement and sizing for an optimal development of the distribution system. In this study, the optimum sizes and locations of DG units are found by considering the power losses and voltage profile as variables into the objective function. The OPTIMIZATION process is done using the link between the Digsilent and Matlab. The results obtained show the improvement of the system in the presence of DGs.

Digital watermarking is one of the best solutions for copyright infringement, copying, data verification, and illegal distribution of digital media. Recently, the protection of digital audio signals has received much attention as one of the fascinating topics for researchers and scholars. In this paper, we presented a new high-capacity, clear, and robust audio signaling scheme BASED on the DWT conversion synergy and golden ratio advantages using the (TLBO) ALGORITHM. We used the (TLBO) ALGORITHM to determine the effective frame length and embedded range, and the golden ratio to determine the appropriate embedded locations for each frame. First, the main audio signal was broken down into several sub-bands using a DWT in a specific frequency range. Since the human auditory system is not sensitive to changes in high-frequency bands, to increase the clarity and capacity of these sub-bands to embed bits we used the watermark signal. Moreover, to increase the resistance to common attacks, we framed the high-frequency bandwidth and then used the average of the frames as a key value. Our main idea was to embed an 8-bit signal simultaneously in the host signal. Experimental results showed that the proposed method is free from significant noticeable distortion (SNR about 29. 68dB) and increases the resistance to common signal processing attacks such as high pass filter, echo, resampling, MPEG (MP3), etc.

The present study is an attempt to propose a mutation-BASED real-coded genetic ALGORITHM (MBRCGA) for sizing and layout OPTIMIZATION of planar and spatial truss structures. The Gaussian mutation operator is used to create the reproduction operators. An adaptive tournament selection mechanism in combination with adaptive Gaussian mutation operators are proposed to achieve an effective search in the design space. The standard deviation of design variables is used as a key factor in the adaptation of mutation operators. The reliability of the proposed ALGORITHM is investigated in typical sizing and layout OPTIMIZATION problems with both discrete and continuous design variables. The numerical results clearly indicated the competitiveness of MBRCGA in comparison with previously presented methods in the literature.