Gravitational search algorithm (GSA) is a recent introduced algorithm which is inspired by law of... more Gravitational search algorithm (GSA) is a recent introduced algorithm which is inspired by law of gravity and mass interactions. In this paper, a novel version of GSA, named Clustered-GSA, is proposed to reduce complexity and computation of the standard GSA. This algorithm is originated from calculating central mass of a system in nature and improves the ability of GSA by reducing the number of objective function evaluations. Clustered-GSA is evaluated on two sets of standard benchmark functions and the results are compared with several heuristic algorithms and a deterministic optimization algorithm. Experimental results show that by using Clustered-GSA, better results are achieved with lower complexity. Moreover, the proposed algorithm is used to optimize the parameters of a Low Noise Amplifier (LNA) in order to achieve the required specifications. LNA is the first stage in a receiver after the antenna. The main performance characteristics of receivers are dictated by the LNA performance. It is necessary to study, design, and optimize all the elements included in the structure, simultaneously. The comparative results show the efficiency of the proposed algorithm.
2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)
Domain adaptation is a method of transfer learning. Domain adaptation has a source domain and tar... more Domain adaptation is a method of transfer learning. Domain adaptation has a source domain and target domain with related but different distributions. Unsupervised domain adaptation could be a scenario wherever we've labeled unlabeled target data and source data. In this paper, an incremental adversarial learning method is proposed for unsupervised domain adaptation. In this work, the unknown target labels are predicted and according to these estimated labels, some target data with more similarity to the source data are added to the source data to improve the adaptation between two domains. We use the adversarial discriminative approach as the base unsupervised domain adaptation technique. We do this to handle the large domain shift between the source and target domain distributions. Experimental reports prove that our approach performs much better on several visual domain adaptation tasks.
Genetics play a prominent role in the development and progression of malignant neoplasms. Identif... more Genetics play a prominent role in the development and progression of malignant neoplasms. Identification of the relevant genes is a high-dimensional data processing problem. Pyramid gravitational search algorithm (PGSA), a hybrid method in which the number of genes is cyclically reduced is proposed to conquer the curse of dimensionality. PGSA consists of two elements, a filter and a wrapper method (inspired by the gravitational search algorithm) which iterates through cycles. The genes selected in each cycle are passed on to the subsequent cycles to further reduce the dimension. PGSA tries to maximize the classification accuracy using the most informative genes while reducing the number of genes. Results are reported on a multi-class microarray gene expression dataset for breast cancer. Several feature selection algorithms have been implemented to have a fair comparison. The PGSA ranked first in terms of accuracy (84.5%) with 73 genes. To check if the selected genes are meaningful i...
Vehicle License Plate Recognition (VLPR) is one of the most important aspects of applying compute... more Vehicle License Plate Recognition (VLPR) is one of the most important aspects of applying computer techniques in Intelligent Transport Systems (ITS). They face difficulties like shadows effects, non-uniform illumination intensity, and dirty plates. To tackle these problems, this paper proposes a new VLPR system by producing a contrast enhancement method, a background removal method, and a binarization method. After binarization, an OCR method using artificial neural network (ANN) reads the plate characters. The performance of the proposed system is tested on 4Â k Iranian vehicle license plate images. The proposed method causes the correct recognition rate of 91.2%. The results obtained in comparison to those of well-known methods show that the proposed system is robust for moving cars in outside environment and under different illumination conditions.
Nowadays, optimization problems are large-scale and complicated, so heuristic optimization algori... more Nowadays, optimization problems are large-scale and complicated, so heuristic optimization algorithms have become common for solving them. Gravitational Search Algorithm (GSA) is one of the heuristic algorithms for solving optimization problems inspired by Newton’s lows of gravity and motion. Definition and calculation of masses in GSA have an impact on the performance of the algorithm. Defining appropriate functions for mass calculation improves the exploitation and exploration power of the algorithm and prevents the algorithm from getting trapped in local optima. In this paper, Sigma scaling and Boltzmann selection functions are examined for mass calculation in GSA. The proposed functions are evaluated on some standard test functions including unimodal functions and multimodal functions. The obtained results are compared with the standard GSA, genetic algorithm, particle swarm optimization algorithm, gravitational particle swarm algorithm and clustered-GSA. Experimental results sh...
Cutaneous leishmaniasis (CL) imposes a major health burden throughout the tropical and subtropica... more Cutaneous leishmaniasis (CL) imposes a major health burden throughout the tropical and subtropical regions of the globe. Unresponsive cases are common phenomena occurred upon exposure to the standard drugs. Therefore, rapid detection, prognosis and classification of the disease are crucial for selecting the proper treatment modality. Using machine learning (ML) techniques, this study aimed to detect unresponsive cases of ACL, caused by Leishmania tropica, which will consequently be used for a more effective treatment modality. This study was conducted as a case-control setting. Patients were selected in a major ACL focus from both unresponsive and responsive cases. Nine unique and relevant features of patients with ACL were selected. To categorize the patients, different classifier models such as k-nearest neighbors (KNN), support vector machines (SVM), multilayer perceptron (MLP), learning vector quantization (LVQ) and multipass LVQ were applied and compared for this supervised lea...
The goal of license plate recognition (LPR) is to read the license plate characters. Due to image... more The goal of license plate recognition (LPR) is to read the license plate characters. Due to image degradation, there are many difficulties in the way of achieving this goal. In this paper, the proposed method recognizes the license plate characters without employing the traditional segmentation and binarization techniques. This method uses a deep learning algorithm and tries to achieve better learning experience by engaging a multi-task learning algorithm based on sharing features. The features of license plate characters are extracted by a deep encoder-decoder network, and transferred to 8 parallel classifiers for recognition. To evaluate the current work, a database of 11,000 license plate images, collected from a currently working surveillance system installed on a dual carriageway, is employed. The proposed method achieved the correct character recognition rate of 96% for 4000 test images that is acceptable in comparison to the competing methods.
Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2018
This paper introduces a multimodal emotion recognition system based on two different modalities, ... more This paper introduces a multimodal emotion recognition system based on two different modalities, i.e., affective speech and facial expression. For affective speech, the common low-level descriptors including prosodic and spectral audio features (i.e., energy, zero crossing rate, MFCC, LPC, PLP and temporal derivatives) are extracted, whereas a novel visual feature extraction method is proposed in the case of facial expression. This method exploits the displacement of specific landmarks across consecutive frames of an utterance for feature extraction. To this end, the time series of temporal variations for each landmark is analyzed individually for extracting primary visual features, and then, the extracted features of all landmarks are concatenated for constructing the final feature vector. The analysis of displacement signal of landmarks is performed by the discrete wavelet transform which is a widely used mathematical transform in signal processing applications. In order to reduce the complexity of derived models and improve the efficiency, a variety of dimensionalityreduction schemes are applied. Furthermore, to exploit the advantages of multimodal emotion recognition systems, the feature-level fusion of the audio and the proposed visual features is examined. Results of experiments conducted on three SAVEE, RML and eNTERFACE05 databases show the efficiency of proposed visual feature extraction method in terms of performance criteria.
An effective method for optimal design of water distribution network (WDN) can significantly bene... more An effective method for optimal design of water distribution network (WDN) can significantly benefit to develop commercial software for component sizing. This research investigates Gravitational Search Algorithm (GSA) for pipe cost optimization model problems. GSA is a meta-heuristic (MH) algorithm which makes ease of its applicability to the design of WDNs due to its minimum number of algorithm parameters and requiring least effort in fine-tuning the parameters. Three well-known benchmark networks (Hanoi network, Two-Reservoir network and New York tunnels network) and a real-world WDN located Khorramshahr city in Iran were used. The GSA results were compared with the solutions obtained through various Evolutionary Algorithms. Experimental results show success of GSA in arriving minimum cost solution. GSA achieved to the best so far solution reported for one case (Hanoi network), and it could find the least cost for three other networks compared to the best results of other optimization algorithms. In addition, for two case studies (Hanoi network and Khorramshahr city network) number of function evaluations were less than other algorithms. Further, the study reveals that GSA achieved the maximum number of times the best so far solution and confirming rapid convergence without struck up at local optimum.
Journal of Intelligent & Robotic Systems, 2017
Technological progresses in the gas sensor fields provide the possibility of designing and constr... more Technological progresses in the gas sensor fields provide the possibility of designing and construction of Electronic nose (Enose) based on the Biological nose. E-nose uses specific hardware and software units; Sensor array is one of the critical units in the E-nose and its types of sensors are determined based on the application. So far, many achievements have been reported for using the E-nose in different fields of application. In this work, an E-nose for handling multipurpose applications is proposed, and the employed hardware and pattern recognition techniques are depicted. To achieve higher recognition rate and lower power consumption, the improved binary gravitational search algorithm (IBGSA) and the K-nearest neighbor (KNN) classifier are used for automatic selecting the best combination of the sensors. The designed E-nose is tested by classifying the odors in different case studies, including moldy bread recognition in food and beverage field, herbs recognition in the medical field, and petroleum products recognition in the industrial field. Experimental results confirm the efficiency of the proposed method for E-nose realization.
Gravitational Search Algorithm (GSA) is an optimization method inspired by the theory of Newtonia... more Gravitational Search Algorithm (GSA) is an optimization method inspired by the theory of Newtonian gravity in physics. Till now, many variants of GSA have been introduced, most of them are motivated by gravity-related theories such as relativity and astronomy. On the one hand, to solve different kinds of optimization problems, modified versions of GSA have been presented such as continuous (real), binary, discrete, multimodal, constraint, single-objective, and multi-objective GSA. On the other hand, to tackle the difficulties in real-world problems, the efficiency of GSA has been improved using specialized operators, hybridization, local search, and designing the self-adaptive algorithms. Researchers have utilized GSA to solve various engineering optimization problems in diverse fields of applications ranging from electrical engineering to bioinformatics. Here, we discussed a comprehensive investigation of GSA and a brief review of GSA developments in solving different engineering problems to build up a global picture and to open the mind to explore possible applications. We also made a number of suggestions that can be undertaken to help move the area forward.
The gravitational search algorithm (GSA) is a meta-heuristic optimization algorithm which is insp... more The gravitational search algorithm (GSA) is a meta-heuristic optimization algorithm which is inspired by the gravity force. This algorithm uses Newton's gravity and motion laws to calculate the masses interactions and shows high performance in solving optimization problems. The premature convergence is the common drawback of heuristic search algorithms in high-dimensional problems, and GSA is not an exception. In this paper, a new version of GSA is proposed to improve the power of GSA in exploration and exploitation. The proposed algorithm has both attractive and repulsive forces. In this algorithm, the heavy particles attract some particles and repulse some others, in which the forces are inversely proportional to their distances. For better evaluation, the GSA with both attractive and repulsive forces (AR-GSA) is tested using CEC 2013 benchmark functions and the results are compared with some well-known meta-heuristic algorithms. The simulation results show that AR-GSA can improve the convergence rate, the exploration, and the exploitation capabilities of GSA.
Pier scour phenomena in the presence of debris accumulation have attracted the attention of engin... more Pier scour phenomena in the presence of debris accumulation have attracted the attention of engineers to present a precise prediction of the local scour depth. Most experimental studies of pier scour depth with debris accumulation have been performed to find an accurate formula to predict the local scour depth. However, an empirical equation with appropriate capacity of validation is not available to evaluate the local scour depth. In this way, gene-expression programming (GEP), evolutionary polynomial regression (EPR), and model tree (MT) based formulations are used to develop to predict the scour depth around bridge piers with debris effects. Laboratory data sets utilized to perform models are collected from different literature. Effective parameters on the local scour depth include geometric characterizations of bridge piers and debris, physical properties of bed sediment, and approaching flow characteristics. The efficiency of the training stages for the GEP, MT, and EPR models ...
Recent Developments in Intelligent Nature-Inspired Computing
Harmony search (HS) is a meta-heuristic search algorithm which tries to mimic the improvisation p... more Harmony search (HS) is a meta-heuristic search algorithm which tries to mimic the improvisation process of musicians in finding a pleasing harmony. In recent years, due to some advantages, HS has received a significant attention. HS is easy to implement, converges quickly to the optimal solution and finds a good enough solution in a reasonable amount of computational time. The merits of HS algorithm have led to its application to optimization problems of different engineering areas. In this chapter, the concepts and performance of HS algorithm are shown and some engineering applications are reviewed. It is observed that HS has shown promising performance in solving difficult optimization problems and different versions of this algorithm have been developed. In the next years, it is expected that HS is applied to more real optimization problems.
Traffic car images suffer immensely from various degrading factors that make it hard to localize ... more Traffic car images suffer immensely from various degrading factors that make it hard to localize license plates. Each license plate localization (LPL) method has its own advantages and disadvantages to extract plates in the images under different circumstances. To have the benefits of different methods, our proposed solution is to employ a combination of four methods including a method based on cascade classifiers and local binary pattern (LBP) features, an edge-based method, a color-based method, and a contrast-based method. Considering the computational complexity, the methods are ordered on the basis of their chances for success. The order of the methods and the parameters are set experimentally in different conditions: day, night, and twilight. Furthermore, to find the plates rapidly, an algorithm is proposed to refine regions of interest (ROIs) and remove unwanted regions. The algorithm is applied in a real automated transport system for plate identification/recognition and tested with 4000 vehicle images taken from a three-lane dual carriageway with a central barrier in the different illumination situations with six cameras. The results are promising in a large database of moving car images. The car license plates have been correctly extracted in 3938 input images (98.45%). The results show that the proposed system is robust for moving cars in outdoor and under different illumination conditions.
Abstract This paper presents an automatic method for finding optimal channels in Brain Computer I... more Abstract This paper presents an automatic method for finding optimal channels in Brain Computer Interfaces (BCIs). Detecting the effective channels in BCI systems is an important problem in reducing the complexity of these systems. In this research, Improved Binary Gravitation Search Algorithm (IBGSA) is used to automatically detect the effective electroencephalography (EEG) channels in left or right hand classification. To do this, at first, data is filtered with a bandpass filter in order to reduce the amount of different types of merged noise. Then, the electrooculography (EOG) and electromyography (EMG) artifacts are corrected based on Blind Source Separation (BSS) algorithm. Data is epoched according to the left or right hand motor imageries and central beta frequency band is isolated for Event Related Synchronization (ERS) analysis. Feature extraction process is carried out by analyzing EEG signals in time and wavelet domains. The logarithmic power of each channel is computed in time domain and the features of mean, mode, median, variance, and standard deviation are calculated in wavelet domain. IBGSA is employed to detect the optimal channels to achieve better classification results. Support Vector Machine (SVM) is used as the classifier. The maximum accuracy of 80% and average accuracy of 76.24% were obtained for eight subjects in BCI competition IV dataset. The results of this research confirm that automatically detecting effective channels can enhance the practical implementation of BCI based systems and reduce the complexity.
Gravitational search algorithm (GSA) is a recent introduced algorithm which is inspired by law of... more Gravitational search algorithm (GSA) is a recent introduced algorithm which is inspired by law of gravity and mass interactions. In this paper, a novel version of GSA, named Clustered-GSA, is proposed to reduce complexity and computation of the standard GSA. This algorithm is originated from calculating central mass of a system in nature and improves the ability of GSA by reducing the number of objective function evaluations. Clustered-GSA is evaluated on two sets of standard benchmark functions and the results are compared with several heuristic algorithms and a deterministic optimization algorithm. Experimental results show that by using Clustered-GSA, better results are achieved with lower complexity. Moreover, the proposed algorithm is used to optimize the parameters of a Low Noise Amplifier (LNA) in order to achieve the required specifications. LNA is the first stage in a receiver after the antenna. The main performance characteristics of receivers are dictated by the LNA performance. It is necessary to study, design, and optimize all the elements included in the structure, simultaneously. The comparative results show the efficiency of the proposed algorithm.
2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS)
Domain adaptation is a method of transfer learning. Domain adaptation has a source domain and tar... more Domain adaptation is a method of transfer learning. Domain adaptation has a source domain and target domain with related but different distributions. Unsupervised domain adaptation could be a scenario wherever we've labeled unlabeled target data and source data. In this paper, an incremental adversarial learning method is proposed for unsupervised domain adaptation. In this work, the unknown target labels are predicted and according to these estimated labels, some target data with more similarity to the source data are added to the source data to improve the adaptation between two domains. We use the adversarial discriminative approach as the base unsupervised domain adaptation technique. We do this to handle the large domain shift between the source and target domain distributions. Experimental reports prove that our approach performs much better on several visual domain adaptation tasks.
Genetics play a prominent role in the development and progression of malignant neoplasms. Identif... more Genetics play a prominent role in the development and progression of malignant neoplasms. Identification of the relevant genes is a high-dimensional data processing problem. Pyramid gravitational search algorithm (PGSA), a hybrid method in which the number of genes is cyclically reduced is proposed to conquer the curse of dimensionality. PGSA consists of two elements, a filter and a wrapper method (inspired by the gravitational search algorithm) which iterates through cycles. The genes selected in each cycle are passed on to the subsequent cycles to further reduce the dimension. PGSA tries to maximize the classification accuracy using the most informative genes while reducing the number of genes. Results are reported on a multi-class microarray gene expression dataset for breast cancer. Several feature selection algorithms have been implemented to have a fair comparison. The PGSA ranked first in terms of accuracy (84.5%) with 73 genes. To check if the selected genes are meaningful i...
Vehicle License Plate Recognition (VLPR) is one of the most important aspects of applying compute... more Vehicle License Plate Recognition (VLPR) is one of the most important aspects of applying computer techniques in Intelligent Transport Systems (ITS). They face difficulties like shadows effects, non-uniform illumination intensity, and dirty plates. To tackle these problems, this paper proposes a new VLPR system by producing a contrast enhancement method, a background removal method, and a binarization method. After binarization, an OCR method using artificial neural network (ANN) reads the plate characters. The performance of the proposed system is tested on 4Â k Iranian vehicle license plate images. The proposed method causes the correct recognition rate of 91.2%. The results obtained in comparison to those of well-known methods show that the proposed system is robust for moving cars in outside environment and under different illumination conditions.
Nowadays, optimization problems are large-scale and complicated, so heuristic optimization algori... more Nowadays, optimization problems are large-scale and complicated, so heuristic optimization algorithms have become common for solving them. Gravitational Search Algorithm (GSA) is one of the heuristic algorithms for solving optimization problems inspired by Newton’s lows of gravity and motion. Definition and calculation of masses in GSA have an impact on the performance of the algorithm. Defining appropriate functions for mass calculation improves the exploitation and exploration power of the algorithm and prevents the algorithm from getting trapped in local optima. In this paper, Sigma scaling and Boltzmann selection functions are examined for mass calculation in GSA. The proposed functions are evaluated on some standard test functions including unimodal functions and multimodal functions. The obtained results are compared with the standard GSA, genetic algorithm, particle swarm optimization algorithm, gravitational particle swarm algorithm and clustered-GSA. Experimental results sh...
Cutaneous leishmaniasis (CL) imposes a major health burden throughout the tropical and subtropica... more Cutaneous leishmaniasis (CL) imposes a major health burden throughout the tropical and subtropical regions of the globe. Unresponsive cases are common phenomena occurred upon exposure to the standard drugs. Therefore, rapid detection, prognosis and classification of the disease are crucial for selecting the proper treatment modality. Using machine learning (ML) techniques, this study aimed to detect unresponsive cases of ACL, caused by Leishmania tropica, which will consequently be used for a more effective treatment modality. This study was conducted as a case-control setting. Patients were selected in a major ACL focus from both unresponsive and responsive cases. Nine unique and relevant features of patients with ACL were selected. To categorize the patients, different classifier models such as k-nearest neighbors (KNN), support vector machines (SVM), multilayer perceptron (MLP), learning vector quantization (LVQ) and multipass LVQ were applied and compared for this supervised lea...
The goal of license plate recognition (LPR) is to read the license plate characters. Due to image... more The goal of license plate recognition (LPR) is to read the license plate characters. Due to image degradation, there are many difficulties in the way of achieving this goal. In this paper, the proposed method recognizes the license plate characters without employing the traditional segmentation and binarization techniques. This method uses a deep learning algorithm and tries to achieve better learning experience by engaging a multi-task learning algorithm based on sharing features. The features of license plate characters are extracted by a deep encoder-decoder network, and transferred to 8 parallel classifiers for recognition. To evaluate the current work, a database of 11,000 license plate images, collected from a currently working surveillance system installed on a dual carriageway, is employed. The proposed method achieved the correct character recognition rate of 96% for 4000 test images that is acceptable in comparison to the competing methods.
Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2018
This paper introduces a multimodal emotion recognition system based on two different modalities, ... more This paper introduces a multimodal emotion recognition system based on two different modalities, i.e., affective speech and facial expression. For affective speech, the common low-level descriptors including prosodic and spectral audio features (i.e., energy, zero crossing rate, MFCC, LPC, PLP and temporal derivatives) are extracted, whereas a novel visual feature extraction method is proposed in the case of facial expression. This method exploits the displacement of specific landmarks across consecutive frames of an utterance for feature extraction. To this end, the time series of temporal variations for each landmark is analyzed individually for extracting primary visual features, and then, the extracted features of all landmarks are concatenated for constructing the final feature vector. The analysis of displacement signal of landmarks is performed by the discrete wavelet transform which is a widely used mathematical transform in signal processing applications. In order to reduce the complexity of derived models and improve the efficiency, a variety of dimensionalityreduction schemes are applied. Furthermore, to exploit the advantages of multimodal emotion recognition systems, the feature-level fusion of the audio and the proposed visual features is examined. Results of experiments conducted on three SAVEE, RML and eNTERFACE05 databases show the efficiency of proposed visual feature extraction method in terms of performance criteria.
An effective method for optimal design of water distribution network (WDN) can significantly bene... more An effective method for optimal design of water distribution network (WDN) can significantly benefit to develop commercial software for component sizing. This research investigates Gravitational Search Algorithm (GSA) for pipe cost optimization model problems. GSA is a meta-heuristic (MH) algorithm which makes ease of its applicability to the design of WDNs due to its minimum number of algorithm parameters and requiring least effort in fine-tuning the parameters. Three well-known benchmark networks (Hanoi network, Two-Reservoir network and New York tunnels network) and a real-world WDN located Khorramshahr city in Iran were used. The GSA results were compared with the solutions obtained through various Evolutionary Algorithms. Experimental results show success of GSA in arriving minimum cost solution. GSA achieved to the best so far solution reported for one case (Hanoi network), and it could find the least cost for three other networks compared to the best results of other optimization algorithms. In addition, for two case studies (Hanoi network and Khorramshahr city network) number of function evaluations were less than other algorithms. Further, the study reveals that GSA achieved the maximum number of times the best so far solution and confirming rapid convergence without struck up at local optimum.
Journal of Intelligent & Robotic Systems, 2017
Technological progresses in the gas sensor fields provide the possibility of designing and constr... more Technological progresses in the gas sensor fields provide the possibility of designing and construction of Electronic nose (Enose) based on the Biological nose. E-nose uses specific hardware and software units; Sensor array is one of the critical units in the E-nose and its types of sensors are determined based on the application. So far, many achievements have been reported for using the E-nose in different fields of application. In this work, an E-nose for handling multipurpose applications is proposed, and the employed hardware and pattern recognition techniques are depicted. To achieve higher recognition rate and lower power consumption, the improved binary gravitational search algorithm (IBGSA) and the K-nearest neighbor (KNN) classifier are used for automatic selecting the best combination of the sensors. The designed E-nose is tested by classifying the odors in different case studies, including moldy bread recognition in food and beverage field, herbs recognition in the medical field, and petroleum products recognition in the industrial field. Experimental results confirm the efficiency of the proposed method for E-nose realization.
Gravitational Search Algorithm (GSA) is an optimization method inspired by the theory of Newtonia... more Gravitational Search Algorithm (GSA) is an optimization method inspired by the theory of Newtonian gravity in physics. Till now, many variants of GSA have been introduced, most of them are motivated by gravity-related theories such as relativity and astronomy. On the one hand, to solve different kinds of optimization problems, modified versions of GSA have been presented such as continuous (real), binary, discrete, multimodal, constraint, single-objective, and multi-objective GSA. On the other hand, to tackle the difficulties in real-world problems, the efficiency of GSA has been improved using specialized operators, hybridization, local search, and designing the self-adaptive algorithms. Researchers have utilized GSA to solve various engineering optimization problems in diverse fields of applications ranging from electrical engineering to bioinformatics. Here, we discussed a comprehensive investigation of GSA and a brief review of GSA developments in solving different engineering problems to build up a global picture and to open the mind to explore possible applications. We also made a number of suggestions that can be undertaken to help move the area forward.
The gravitational search algorithm (GSA) is a meta-heuristic optimization algorithm which is insp... more The gravitational search algorithm (GSA) is a meta-heuristic optimization algorithm which is inspired by the gravity force. This algorithm uses Newton's gravity and motion laws to calculate the masses interactions and shows high performance in solving optimization problems. The premature convergence is the common drawback of heuristic search algorithms in high-dimensional problems, and GSA is not an exception. In this paper, a new version of GSA is proposed to improve the power of GSA in exploration and exploitation. The proposed algorithm has both attractive and repulsive forces. In this algorithm, the heavy particles attract some particles and repulse some others, in which the forces are inversely proportional to their distances. For better evaluation, the GSA with both attractive and repulsive forces (AR-GSA) is tested using CEC 2013 benchmark functions and the results are compared with some well-known meta-heuristic algorithms. The simulation results show that AR-GSA can improve the convergence rate, the exploration, and the exploitation capabilities of GSA.
Pier scour phenomena in the presence of debris accumulation have attracted the attention of engin... more Pier scour phenomena in the presence of debris accumulation have attracted the attention of engineers to present a precise prediction of the local scour depth. Most experimental studies of pier scour depth with debris accumulation have been performed to find an accurate formula to predict the local scour depth. However, an empirical equation with appropriate capacity of validation is not available to evaluate the local scour depth. In this way, gene-expression programming (GEP), evolutionary polynomial regression (EPR), and model tree (MT) based formulations are used to develop to predict the scour depth around bridge piers with debris effects. Laboratory data sets utilized to perform models are collected from different literature. Effective parameters on the local scour depth include geometric characterizations of bridge piers and debris, physical properties of bed sediment, and approaching flow characteristics. The efficiency of the training stages for the GEP, MT, and EPR models ...
Recent Developments in Intelligent Nature-Inspired Computing
Harmony search (HS) is a meta-heuristic search algorithm which tries to mimic the improvisation p... more Harmony search (HS) is a meta-heuristic search algorithm which tries to mimic the improvisation process of musicians in finding a pleasing harmony. In recent years, due to some advantages, HS has received a significant attention. HS is easy to implement, converges quickly to the optimal solution and finds a good enough solution in a reasonable amount of computational time. The merits of HS algorithm have led to its application to optimization problems of different engineering areas. In this chapter, the concepts and performance of HS algorithm are shown and some engineering applications are reviewed. It is observed that HS has shown promising performance in solving difficult optimization problems and different versions of this algorithm have been developed. In the next years, it is expected that HS is applied to more real optimization problems.
Traffic car images suffer immensely from various degrading factors that make it hard to localize ... more Traffic car images suffer immensely from various degrading factors that make it hard to localize license plates. Each license plate localization (LPL) method has its own advantages and disadvantages to extract plates in the images under different circumstances. To have the benefits of different methods, our proposed solution is to employ a combination of four methods including a method based on cascade classifiers and local binary pattern (LBP) features, an edge-based method, a color-based method, and a contrast-based method. Considering the computational complexity, the methods are ordered on the basis of their chances for success. The order of the methods and the parameters are set experimentally in different conditions: day, night, and twilight. Furthermore, to find the plates rapidly, an algorithm is proposed to refine regions of interest (ROIs) and remove unwanted regions. The algorithm is applied in a real automated transport system for plate identification/recognition and tested with 4000 vehicle images taken from a three-lane dual carriageway with a central barrier in the different illumination situations with six cameras. The results are promising in a large database of moving car images. The car license plates have been correctly extracted in 3938 input images (98.45%). The results show that the proposed system is robust for moving cars in outdoor and under different illumination conditions.
Abstract This paper presents an automatic method for finding optimal channels in Brain Computer I... more Abstract This paper presents an automatic method for finding optimal channels in Brain Computer Interfaces (BCIs). Detecting the effective channels in BCI systems is an important problem in reducing the complexity of these systems. In this research, Improved Binary Gravitation Search Algorithm (IBGSA) is used to automatically detect the effective electroencephalography (EEG) channels in left or right hand classification. To do this, at first, data is filtered with a bandpass filter in order to reduce the amount of different types of merged noise. Then, the electrooculography (EOG) and electromyography (EMG) artifacts are corrected based on Blind Source Separation (BSS) algorithm. Data is epoched according to the left or right hand motor imageries and central beta frequency band is isolated for Event Related Synchronization (ERS) analysis. Feature extraction process is carried out by analyzing EEG signals in time and wavelet domains. The logarithmic power of each channel is computed in time domain and the features of mean, mode, median, variance, and standard deviation are calculated in wavelet domain. IBGSA is employed to detect the optimal channels to achieve better classification results. Support Vector Machine (SVM) is used as the classifier. The maximum accuracy of 80% and average accuracy of 76.24% were obtained for eight subjects in BCI competition IV dataset. The results of this research confirm that automatically detecting effective channels can enhance the practical implementation of BCI based systems and reduce the complexity.
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Papers by Esmat Rashedi