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1.	Al Bataineh, A., et al. (author) An efficient hybrid extreme learning machine and evolutionary framework with applications for medical diagnosis 2024 In: Expert systems (Print). - : Wiley. - 0266-4720 .- 1468-0394. ; 41:4 Journal article (peer-reviewed)abstract Integrating machine learning techniques into medical diagnostic systems holds great promise for enhancing disease identification and treatment. Among the various options for training such systems, the extreme learning machine (ELM) stands out due to its rapid learning capability and computational efficiency. However, the random selection of input weights and hidden neuron biases in the ELM can lead to suboptimal performance. To address this issue, our study introduces a novel approach called modified Harris hawks optimizer (MHHO) to optimize these parameters in ELM for medical classification tasks. By applying the MHHO-based method to seven medical datasets, our experimental results demonstrate its superiority over seven other evolutionary-based ELM trainer models. The findings strongly suggest that the MHHO approach can serve as a valuable tool for enhancing the performance of ELM in medical diagnosis.
2.	Amirsadri, Shima, et al. (author) A Levy flight-based grey wolf optimizer combined with back-propagation algorithm for neural network training 2017 In: Neural Computing & Applications. - : Springer Nature. - 0941-0643 .- 1433-3058. ; 30:12, s. 3707-3720 Journal article (peer-reviewed)abstract In the present study, a new algorithm is developed for neural network training by combining a gradient-based and a meta-heuristic algorithm. The new algorithm benefits from simultaneous local and global search, eliminating the problem of getting stuck in local optimum. For this purpose, first the global search ability of the grey wolf optimizer (GWO) is improved with the Levy flight, a random walk in which the jump size follows the Levy distribution, which results in a more efficient global search in the search space thanks to the long jumps. Then, this improved algorithm is combined with back propagation (BP) to use the advantages of enhanced global search ability of GWO and local search ability of BP algorithm in training neural network. The performance of the proposed algorithm has been evaluated by comparing it against a number of well-known meta-heuristic algorithms using twelve classification and function-approximation datasets.
3.	Bojnordi, Ehsan, et al. (author) Improving the Generalisation Ability of Neural Networks Using a Lévy Flight Distribution Algorithm for Classification Problems 2023 In: New generation computing. - : Springer Nature. - 0288-3635 .- 1882-7055. ; 41:2, s. 225-242 Journal article (peer-reviewed)abstract While multi-layer perceptrons (MLPs) remain popular for various classification tasks, their application of gradient-based schemes for training leads to some drawbacks including getting trapped in local optima. To tackle this, population-based metaheuristic methods have been successfully employed. Among these, Lévy flight distribution (LFD), which explores the search space through random walks based on a Lévy distribution, has shown good potential to solve complex optimisation problems. LFD uses two main components, the step length of the walk and the movement direction, for random walk generation to explore the search space. In this paper, we propose a novel MLP training algorithm based on the Lévy flight distribution algorithm for neural network-based pattern classification. We encode the network’s parameters (i.e., its weights and bias terms) into a candidate solution for LFD, and employ the classification error as fitness function. The network parameters are then optimised, using LFD, to yield an MLP that is trained to perform well on the classification task at hand. In an extensive set of experiments, we compare our proposed algorithm with a number of other approaches, including both classical algorithms and other metaheuristic approaches, on a number of benchmark classification problems. The obtained results clearly demonstrate the superiority of our LFD training algorithm.
4.	Casas-Ordaz, A., et al. (author) Enhancing image thresholding segmentation with a novel hybrid battle royale optimization algorithm 2024 In: Multimedia tools and applications. - : Springer Nature. - 1380-7501 .- 1573-7721. Journal article (peer-reviewed)abstract Accurate image segmentation is crucial in digital image processing, enabling efficient image analysis and robust vision systems. However, segmentation is a complex task as images vary in their characteristics, and the computational costs increase with the number of classes involved. To address these challenges, incorporating metaheuristic algorithms to guide the segmentation process presents an exciting opportunity for improvement. This research paper introduces a novel multilevel image segmentation approach that leverages a hybrid battle royale optimization algorithm. By combining opposition-based learning, highly disruptive polynomial mutation, differential evolution mutation, and crossover operators, the proposed method enhances the original battle royale optimization algorithm and effectively solves the segmentation problem. To evaluate the effectiveness of the proposed approach, the minimum cross-entropy criterion is applied to two sets of reference images that undergo multilevel thresholding with up to five thresholds. The results are compared with those obtained using nine other metaheuristic algorithms, employing various image quality metrics such as peak signal noise ratio, structural similarity index method, feature similarity index method, quality index based on local variance, Haar wavelet-based perceptual similarity index, and universal image quality index. The results are analyzed quantitatively, qualitatively, and statistically. The findings demonstrate the potential of the proposed approach in achieving high-quality multilevel thresholding image segmentation. Additionally, the hybrid battle royale optimization algorithm showcases its robustness and efficiency when compared to the other metaheuristic algorithms tested. Notable results are PSNR = 2.13E+01, SSIM = 8.41E-01, FSIM = 8.42E-01, QILV = 8.94E-01, HPSI = 6.52E-01, and UIQI = 9.78E-01.
5.	Esmaeili, Leila, et al. (author) An efficient method to minimize cross-entropy for selecting multi-level threshold values using an improved human mental search algorithm 2021 In: Expert systems with applications. - : Elsevier BV. - 0957-4174 .- 1873-6793. ; 182 Journal article (peer-reviewed)abstract The minimum cross-entropy (MCIT) is introduced as a multi-level image thresholding approach, but it suffers from time complexity, in particular, when the number of thresholds is high. To address this issue, this paper proposes a novel MCIT-based image thresholding based on improved human mental search (HMS) algorithm, a recently proposed population-based metaheuristic algorithm to tackle complex optimisation problems. To further enhance the efficacy, we improve HMS algorithm, IHMSMLIT, with four improvements, including, adaptively selection of the number of mental searches instead of randomly selection, proposing one-step k-means clustering for region clustering, updating based on global and personal experiences, and proposing a random clustering strategy. To assess our proposed algorithm, we conduct an extensive set of experiments with several state-of-the-art and the most recent approaches on a benchmark set of images and in terms of several criteria including objective function, peak signal to noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), and stability analysis. The obtained results apparently demonstrate the competitive performance of our proposed algorithm.
6.	Helali Moghadam, Mahshid, et al. (author) Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing 2022 Reports (other academic/artistic)abstract This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed version, we utilize a new set of bio-inspired search algorithms, genetic algorithm (GA), (μ+ λ) and (μ,λ) evolution strategies(ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain-specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber-physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure-revealing test scenarios for testing an ML-driven lane-keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints.
7.	Helali Moghadam, Mahshid, et al. (author) Machine learning testing in an ADAS case study using simulation-integrated bio-inspired search-based testing 2024 In: Journal of Software. - : John Wiley and Sons Ltd. - 2047-7473 .- 2047-7481. ; :5 Journal article (peer-reviewed)abstract This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed version, we utilize a new set of bio-inspired search algorithms, genetic algorithm (GA), (Formula presented.) and (Formula presented.) evolution strategies (ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain-specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber-physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure-revealing test scenarios for testing an ML-driven lane-keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints.
8.	lehi, Arash Yunessnia, et al. (author) Pre-treatment of textile wastewaters containing Chrysophenine using hybrid membranes 2017 In: Membrane Water Treatment. - : Techno-Press. - 2005-8624. ; 8:1, s. 89-112 Journal article (peer-reviewed)abstract Dyeing wastewaters are the most problematic wastewater in textile industries and also, growing amounts of waste fibers in carpet industries have concerned environmental specialists. Among different treatment methods, membrane filtration processes as energy-efficient and compatible way, were utilized for several individual problems. In this research, novel hybrid membranes were prepared by waste fibers of mechanical carpets as useful resource of membrane matrix and industrial graphite powder as filler to eliminate Chrysophenine GX from dyeing wastewater. These membranes were expected to be utilized for first stage of hybrid membrane filtration process including (adsorption-ultrafiltration) and nanofiltration in Kashan Textile Company. For scaling of membrane filtration process, fouling mechanism of these membranes were recognized and explained by the use of genetic algorithm, as well. The graphite increased rejection and diminished permeate flux at low concentration but in high concentration, the performance was significantly worsened. Among all hybrid membranes, 18% wt. waste fibers-1% wt. graphite membrane had the best performance and minimum fouling. The maximum pore size of this optimum membrane was ranged from 16.10 to 18.72 nm
9.	Mohammadigheymasi, Hamzeh, et al. (author) A data set of earthquake bulletin and seismic waveforms for Ghana obtained by deep learning 2023 In: Data in Brief. - : Elsevier BV. - 2352-3409. ; 47, s. 108969-108969 Journal article (peer-reviewed)abstract The Ghana Digital Seismic Network (GHDSN) data, with six broadband sensors, operating in southern Ghana for two years (2012-2014). The recorded dataset is processed for simultaneous event detection and phase picking by a Deep Learning (DL) model, the EQTransformer tool. Here, the detected earthquakes consisting of supporting data, waveforms (including P and S arrival phases), and earthquake bulletin are presented. The bulletin includes the 559 arrival times (292 P and 267 S phases) and waveforms of the 73 local earthquakes in SEISAN format. The supporting data encompasses the preliminary crustal velocity models obtained from the joint inversion analysis of the detected hypocentral parameters. These parameters comprised of a 6- layer model of the crustal velocity (Vp and Vp/Vs ratio), incident time sequence, and statistical analysis of the detected earthquakes and hypocentral parameters analyzed and relocated by the updated crustal velocity and graphic representation of them a 3D live figure enlighting the seismogenic depth of the region. This dataset has a unique appeal for earth science specialists to analyze and reprocess the detected waveforms and characterize the seismogenic sources and active faults in Ghana. The metadata and waveforms have been deposited at the Mendeley Data repository
10.	Morales-Castañeda, Bernardo, et al. (author) A Novel Diversity-Aware Inertia Weight and Velocity Control for Particle Swarm Optimization 2023 In: 2023 IEEE Congress on Evolutionary Computation (CEC). - : IEEE Press. - 9798350314588 - 9798350314588 Conference paper (peer-reviewed)abstract Particle Swarm Optimization (PSO) has efficiently solved several real-world applications and optimization problems. However, it has shortcomings, such as premature convergence and stagnation at local minima. Inertia weight is a parameter of this algorithm that controls the global and local exploration and exploitation capability by determining the influence of the previous velocity on its current motion. Therefore, this article proposes a PSO with a Diversity-aware Inertia and Velocity Control (PSOIVC) algorithm to improve the PSO performance. The PSOIVC employs a novel diversity-aware inertia weight and velocity control approach to tune the parameters to produce a trade-off between exploration and exploitation of the algorithm using the dimension-wise diversity. The PSOIVC algorithm is compared with eight algorithms, including variants of the PSO, on a set of 30 benchmark functions for a single objective real parameter in 30 and 50 dimensions. Based on the results, the proposal presents significant outcomes according to the average values obtained for both comparisons; because it performed similarly or better than the other algorithms in 23/30 and 16/30 for 30 and 50 dimensions, respectively.
11.	Morales-Castañeda, Bernardo, et al. (author) Improving the Convergence of the PSO Algorithm with a Stagnation Variable and Fuzzy Logic 2023 In: 2023 IEEE Congress on Evolutionary Computation (CEC). - : IEEE Press. - 9798350314588 - 9798350314588 Conference paper (peer-reviewed)abstract Particle swarm optimization (PSO) is essential to evolutionary computation algorithms (ECA). The PSO has some drawbacks as premature convergence and stagnation at local minima. Inertia weight is a parameter that controls the global and local exploration and exploitation capability in the PSO by determining the influence of the previous velocity on its current motion. This article proposes using a stagnation counter that verifies the times the PSO is stuck in the same fitness value. In the proposed fuzzy controlled PSO with stagnation coefficient (FCPSO), a fuzzy controller is designed to tune the inertia weight based on the population's diversity and the search's stagnation. This modification allows the PSO to escape from suboptimal values enhancing its search capabilities. The FCPSO is tested over 28 benchmark functions in 50 dimensions. Besides, it has been compared with nine optimization algorithms from the state-of-the-art. The experiments and comparisons suggest that the FCPSO is an interesting tool for solving complex optimization problems.
12.	Oliva, Diego, et al. (author) A hyper-heuristic guided by a probabilistic graphical model for single-objective real-parameter optimization 2022 In: International Journal of Machine Learning and Cybernetics. - : Springer Nature. - 1868-8071 .- 1868-808X. ; 13:12, s. 3743-3772 Journal article (peer-reviewed)abstract Metaheuristics algorithms are designed to find approximate solutions for challenging optimization problems. The success of the algorithm over a given optimization task relies on the suitability of its search heuristics for the problem-domain. Thus, the design of custom metaheuristic algorithms leads to more accurate solutions. Hyper-heuristics (HH) are important tools commonly used to select low-level heuristics (LLHs) to solve a specific problem. HH are able to acquire knowledge from the problems where they are used. However, as other artificial intelligence tools it is necessary to identify how the knowledge affects the performance of the algorithm. One way to generate such knowledge is to capture interactions between variables using probabilistic graphical models such as Bayesian networks (BN) in conjunction with estimation of distribution algorithms (EDA). This article presents a method based on that used an EDA based on BN as a high-level selection mechanism for HH called Hyper-heuristic approach based on Bayesian learning and evolutionary operators (HHBNO). Here the knowledge is extracted form BN to evolve the sequences of LLHs in an online learning process by exploring the inter-dependencies among the LLHs. The proposes approach is tested over CEC’17 set of benchmark function of single-objective real-parameter optimization. Statical tests verifies that the HHBNO presents competitive results in comparison with other metaheuristic algorithms with high performance in terms of convergence. The generated BN is further visually investigated to display the acquired knowledge during the evolutionary process, and it is constructed with the probabilities of each LLHs.
13.	Oliva, Diego, et al. (author) Segmentation of thermographies from electronic systems by using the global-best brain storm optimization algorithm 2023 In: Multimedia tools and applications. - : Springer Nature. - 1380-7501 .- 1573-7721. ; 82:29, s. 44911-44941 Journal article (peer-reviewed)abstract Segmentation is an important and basic task in image processing. Although no unique method is applicable to all types of images (as thermographies), multilevel thresholding is one of the most widely used techniques for this purpose. Multilevel thresholding segmentation has a major drawback that is to properly find the best configuration of thresholds. For that reason some metaheuristic algorithms are used to optimize the searching for the best thresholds. This paper proposes a combination of the minimum cross-entropy method and the Global-best brain storm optimization algorithm (GBSO), which improves the standard BSO to find the optimal solutions in complex search spaces. The GBSO uses a population of agents based on a global best and a re-initialization scheme that is triggered by the current state of its population. Here, the GBSO is used to find the best configuration of thresholds by optimizing the minimum cross entropy that is commonly using in image segmentation. Once the best thresholds are obtained they are applied over the images to extract only the regions of interest. For example, in the case of thermographies the parts with higher temperatures. To verify the performance of the proposed method it is firstly applied to classical reference images and after that over thermal images from electronic devices. The idea is to provide an alternative to segment thermographies that permits separating regions with higher temperatures. This could be used as a preprocessing step in a complex image processing system. The experimental result in terms of segmentation of electronic devices in thermographies provides evidence of the good performance of the GBSO. Different comparison with recent methods from the state-of-the-art were conducted where the GBSO obtains 1st place with the best values for the MCET. To validate the quality of segmentation they were used metrics as the peak signal-to-noise ratio (PSNR) where the GBSO is in the 4th rank of comparison, the structural similarity index (SSIM) and the feature similarity index (FSIM). For the FSIM and SSIM the GBSO in the 4th and 3rd rank, respectively.
14.	Ramos-Michel, A., et al. (author) Improving Metaheuristic Algorithm Design Through Inequality and Diversity Analysis : A Novel Multi-Population Differential Evolution 2023 In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). - : IEEE. - 9781665430654 ; , s. 1547-1552 Conference paper (peer-reviewed)abstract In evolutionary algorithms and metaheuristics, defining when applying a specific operator is important. Besides, in complex optimization problems, multiple populations can be used to explore the search space simultaneously. However, one of the main problems is extracting information from the populations and using it to evolve the solutions. This article presents the inequality-based multi-population differential evo-lution (IMDE). This algorithm uses the K-means to generate subpopulations (settlements). Two variables are extracted from the settlements, the diversity and the Gini index, which measure the solutions' distribution and the solutions' inequality regarding fitness. The Gini index and the diversity are used in the IMDE to dynamically modify the scalation factor and the crossover rate. Experiments over a set of benchmark functions with different degrees of complexity validate the performance of the IMDE. Besides comparisons, statistical and ranking average validate the search capabilities of the IMDE.
15.	Rezaee, Khosro, et al. (author) An Autonomous UAV-Assisted Distance-Aware Crowd Sensing Platform Using Deep ShuffleNet Transfer Learning 2022 In: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 23:7, s. 9404-9413 Journal article (peer-reviewed)abstract Autonomous unmanned aerial vehicles (UAVs) are essential for detecting and tracking specific events, such as automatic navigation. The intelligent monitoring of people’s social distances in crowds is one of the most significant events caused by the coronavirus. The virus is spreading more quickly among the crowds, and the disease cycle continues in congested areas. Due to the error that occurs when humans monitor their activity, an automated model is required to alert to social distance violations in crowds. As a result, this article proposes a two-step framework based on autonomous UAV videos, including human tracking and deep learning-based recognition of the crowd’s social distance. The deep architecture is a modified-fast and lightweight ShuffleNet learning structure. First, the Kalman filter is used to determine the positions of individuals, and then the modified ShuffleNet is used to refine the bounding boxes obtained and determine the social distance. The social distance is calculated using the initial refinement of the bounding box obtained during the tracking step and the scale in frames of the human body. The observed average accuracy, average processing time (APT), and processed frame per second (FPS) for three congestion datasets were 97.5%, 84 milliseconds, and 11.5 FPS, respectively. Real-time decision-making was achieved by reducing the size and resolution of the frames. Additionally, the frames were re-labeled to reduce the computational complexity associated with detecting social distancing. The experimental results demonstrated that the proposed method could operate more quickly and accurately on various resolution frames of UAV videos with difficult conditions.
16.	Seyed Jalaleddin, Mousavirad, et al. (author) A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation 2021 In: Entropy. - : MDPI AG. - 1099-4300. ; 24:1 Journal article (peer-reviewed)abstract Masi entropy is a popular criterion employed for identifying appropriate threshold values in image thresholding. However, with an increasing number of thresholds, the efficiency of Masi entropy-based multi-level thresholding algorithms becomes problematic. To overcome this, we propose a novel differential evolution (DE) algorithm as an effective population-based metaheuristic for Masi entropy-based multi-level image thresholding. Our ME-GDEAR algorithm benefits from a grouping strategy to enhance the efficacy of the algorithm for which a clustering algorithm is used to partition the current population. Then, an updating strategy is introduced to include the obtained clusters in the current population. We further improve the algorithm using attraction (towards the best individual) and repulsion (from random individuals) strategies. Extensive experiments on a set of benchmark images convincingly show ME-GDEAR to give excellent image thresholding performance, outperforming other metaheuristics in 37 out of 48 cases based on cost function evaluation, 26 of 48 cases based on feature similarity index, and 20 of 32 cases based on Dice similarity. The obtained results demonstrate that population-based metaheuristics can be successfully applied to entropy-based image thresholding and that strengthening both exploitation and exploration strategies, as performed in ME-GDEAR, is crucial for designing such an algorithm.
17.	Seyed Jalaleddin, Mousavirad, et al. (author) A memetic imperialist competitive algorithm with chaotic maps for multi-layer neural network training 2019 In: International Journal of Bio-Inspired Computation (IJBIC). - : Inderscience Publishers. - 1758-0366 .- 1758-0374. ; 14:4, s. 227-227 Journal article (peer-reviewed)abstract The performance of artificial neural networks (ANNs) is largely dependent on the success of the training process. Gradient descent-based methods are the most widely used training algorithms but have drawbacks such as ending up in local minima. One approach to overcome this is to use population-based algorithms such as the imperialist competitive algorithm (ICA) which is inspired by the imperialist competition between countries. In this paper, we present a new memetic approach for neural network training to improve the efficacy of ANNs. Our proposed approach - memetic imperialist competitive algorithm with chaotic maps (MICA-CM) - is based on a memetic ICA and chaotic maps, which are responsible for exploration of the search space, while back-propagation is used for an effective local search on the best solution obtained by ICA. Experimental results confirm our proposed algorithm to be highly competitive compared to other recently reported methods.
18.	Seyed Jalaleddin, Mousavirad, et al. (author) A Novel Two-Level Clustering-Based Differential Evolution Algorithm for Training Neural Networks 2024 In: Lecture Notes in Computer Science. - : Springer Nature. - 9783031568510 ; , s. 259-272 Conference paper (peer-reviewed)abstract Determining appropriate weights and biases for feed-forward neural networks is a critical task. Despite the prevalence of gradient-based methods for training, these approaches suffer from sensitivity to initial values and susceptibility to local optima. To address these challenges, we introduce a novel two-level clustering-based differential evolution approach, C2L-DE, to identify the initial seed for a gradient-based algorithm. In the initial phase, clustering is employed to detect some regions in the search space. Population updates are then executed based on the information available within each region. A new central point is proposed in the subsequent phase, leveraging cluster centres for incorporation into the population. Our C2L-DE algorithm is compared against several recent DE-based neural network training algorithms, and is shown to yield favourable performance.
19.	Seyed Jalaleddin, Mousavirad, et al. (author) A Novel Two-Level Clustering-Based Differential Evolution Algorithm for Training Neural Networks 2024 In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - : Springer Science and Business Media Deutschland GmbH. - 9783031568510 ; , s. 259-272 Conference paper (peer-reviewed)abstract Determining appropriate weights and biases for feed-forward neural networks is a critical task. Despite the prevalence of gradient-based methods for training, these approaches suffer from sensitivity to initial values and susceptibility to local optima. To address these challenges, we introduce a novel two-level clustering-based differential evolution approach, C2L-DE, to identify the initial seed for a gradient-based algorithm. In the initial phase, clustering is employed to detect some regions in the search space. Population updates are then executed based on the information available within each region. A new central point is proposed in the subsequent phase, leveraging cluster centres for incorporation into the population. Our C2L-DE algorithm is compared against several recent DE-based neural network training algorithms, and is shown to yield favourable performance.
20.	Seyed Jalaleddin, Mousavirad, et al. (author) A transfer learning based artificial neural network in geometrical design of textured surfaces for tribological applications 2023 In: Surface Topography: Metrology and Properties. - : IOP Publishing. - 2051-672X. ; 11:2 Journal article (peer-reviewed)abstract This study aims at introducing the potential to utilise transfer learning methods in the training of artificial neural networks for tribological applications. Artificially enhanced surfaces through surface texturing, as an example, are investigated under hydrodynamic regime of lubrication. The performance of these surface features is assessed in terms of load carrying capacity and friction. A large performance dataset including bearing load carrying capacity and friction is initially obtained for a specific category of textures with rectangular cross-sectional profile through analytical methods. The produced bearing performance are used to train a neural network. This neural network was then trained further by a minimal set of performance measure data from an intended category of textures with triangular cross-sectional profiles. It is shown that the resulting neural network performs with acceptable level of confidence for those intended texture profiles when trained with such relatively low number of performance data points. The results indicate that fast analytical methods can potentially produce a large volume of training datasets, which effectively allows for use of relatively lower number of training data sets from the intended category, where creating data for trainings can be more complex or time consuming. Use of transfer learning method in tribological applications and use of bearing performance parameters, as opposed to bearing design parameters, for training the neural networks are the major novel contributions of this study, which has not hitherto been reported elsewhere.
21.	Seyed Jalaleddin, Mousavirad, et al. (author) Automatic clustering using a local search-based human mental search algorithm for image segmentation 2020 In: Applied Soft Computing. - : Elsevier BV. - 1568-4946 .- 1872-9681. ; 96 Journal article (peer-reviewed)abstract Clustering is a commonly employed approach to image segmentation. To overcome the problems of conventional algorithms such as getting trapped in local optima, in this paper, we propose an improved automatic clustering algorithm for image segmentation based on the human mental search (HMS) algorithm, a recently proposed method to solve complex optimisation problems. In contrast to most existing methods for image clustering, our approach does not require any prior knowledge about the number of clusters but rather determines the optimal number of clusters automatically. In addition, for further improved efficacy, we incorporate local search operators which are designed to make changes to the current cluster configuration.To evaluate the performance of our proposed algorithm, we perform an extensive comparison with several state-of-the-art algorithms on a benchmark set of images and using a variety of metrics including cost function, correctness of the obtained numbers of clusters, stability, as well as supervised and unsupervised segmentation criteria. The obtained results clearly indicate excellent performance compared to existing methods with our approach yielding the best result in 16 of 17 cases based on cost function evaluation, 9 of 11 cases based on number of identified clusters, 13 of 17 cases based on the unsupervised Borsotti image segmentation criterion, and 7 of 11 cases based on the supervised PRI image segmentation metric.
22.	Seyed Jalaleddin, Mousavirad, et al. (author) Effective image clustering based on human mental search 2019 In: Applied Soft Computing. - : Elsevier BV. - 1568-4946 .- 1872-9681. ; 78, s. 209-220 Journal article (peer-reviewed)abstract Image segmentation is one of the fundamental techniques in image analysis. One group of segmentation techniques is based on clustering principles, where association of image pixels is based on a similarity criterion. Conventional clustering algorithms, such as -means, can be used for this purpose but have several drawbacks including dependence on initialisation conditions and a higher likelihood of converging to local rather than global optima.In this paper, we propose a clustering-based image segmentation method that is based on the human mental search (HMS) algorithm. HMS is a recent metaheuristic algorithm based on the manner of searching in the space of online auctions. In HMS, each candidate solution is called a bid, and the algorithm comprises three major stages: mental search, which explores the vicinity of a solution using Levy flight to find better solutions; grouping which places a set of candidate solutions into a group using a clustering algorithm; and moving bids toward promising solution areas. In our image clustering application, bids encode the cluster centres and we evaluate three different objective functions.In an extensive set of experiments, we compare the efficacy of our proposed approach with several state-of-the-art metaheuristic algorithms including a genetic algorithm, differential evolution, particle swarm optimisation, artificial bee colony algorithm, and harmony search. We assess the techniques based on a variety of metrics including the objective functions, a cluster validity index, as well as unsupervised and supervised image segmentation criteria. Moreover, we perform some tests in higher dimensions, and conduct a statistical analysis to compare our proposed method to its competitors. The obtained results clearly show that the proposed algorithm represents a highly effective approach to image clustering that outperforms other state-of-the-art techniques.
23.	Seyed Jalaleddin, Mousavirad, et al. (author) Energy-aware JPEG image compression : A multi-objective approach 2023 In: Applied Soft Computing. - : Elsevier BV. - 1568-4946 .- 1872-9681. ; 141 Journal article (peer-reviewed)abstract Customer satisfaction is crucially affected by energy consumption in mobile devices. One of the most energy-consuming parts of an application is images. This paper, first, investigates that there is a correlation between energy consumption and image quality as well as image file size. Therefore, these two can be considered as a proxy for energy consumption. In the next step, we focused on proposing a multi-objective strategy to enhance image quality and reduce image file size based on the quantisation table (QT) in JPEG image compression. To this end, we have used two general multi-objective approaches: scalarisation and Pareto-based. In this paper, we embed our strategy into five scalarisation algorithms, including energy-aware multi-objective genetic algorithm (EnMOGA), energy-aware multi-objective particle swarm optimisation (EnMOPSO), energy-aware multi-objective differential evolution (EnMODE), energy-aware multi-objective evolutionary strategy (EnMOES), and energy-aware multi-objective pattern search (EnMOPS). Also, two Pareto-based methods, including a non-dominated sorting genetic algorithm (NSGA-II) and a reference-point-based NSGA-II (NSGA-III) are used for the embedding scheme, and two Pareto-based algorithms, EnNSGAII and EnNSGAIII, are presented. With our proposed scalarisation method, user’s preferences can be set before starting the optimisation process and the algorithm generates only one solution based on the preference, while our Pareto-based approaches generate a set of solutions so that a user can select one of the preferred solutions after the optimisation process.Experimental studies show that the performance of the baseline algorithm is improved by embedding the proposed strategy into metaheuristic algorithms. In particular, EnMOGA, EnMOPS, and EnNSGA-II can perform competitively, among others. From the results, the baseline algorithm in all cases and in comparison to all algorithms yields the worst results. Among the scalarisation methods, EnMOGA and EnMOPS can achieve the first rank in 6 and 7 out of 13 cases and the second rank in 7 and 5 cases in terms of objective function. Also, EnMOES achieved the fifth or worst rank among the scalarisation algorithms. Regarding the Pareto-based algorithms, the table shows that EnNSGAII outperforms EnNSGAIII in 10 out of 13 cases in terms of hyper-volume measure, while it fails in 3 cases. Furthermore, we statistically verify the proposed algorithm’s effectiveness based on the Wilcoxon-signed rank test. Finally, a sensitivity analysis of the parameters is provided.
24.	Seyed Jalaleddin, Mousavirad, et al. (author) How effective are current population-based metaheuristic algorithms for variance-based multi-level image thresholding? 2023 In: Knowledge-Based Systems. - : Elsevier B.V.. - 0950-7051 .- 1872-7409. ; 272 Journal article (peer-reviewed)abstract Multi-level image thresholding is a common approach to image segmentation where an image is divided into several regions based on its histogram. Otsu's method is the most popular method for this purpose, and is based on seeking for threshold values that maximise the between-class variance. This requires an exhaustive search to find the optimal set of threshold values, making image thresholding a time-consuming process. This is especially the case with increasing numbers of thresholds since, due to the curse of dimensionality, the search space enlarges exponentially with the number of thresholds. Population-based metaheuristic algorithms are efficient and effective problem-independent methods to tackle hard optimisation problems. Over the years, a variety of such algorithms, often based on bio-inspired paradigms, have been proposed. In this paper, we formulate multi-level image thresholding as an optimisation problem and perform an extensive evaluation of 23 population-based metaheuristics, including both state-of-the-art and recently introduced algorithms, for this purpose. We benchmark the algorithms on a set of commonly used images and based on various measures, including objective function value, peak signal-to-noise ratio, feature similarity index, and structural similarity index. In addition, we carry out a stability analysis as well as a statistical analysis to judge if there are significant differences between algorithms. Our experimental results indicate that recently introduced algorithms do not necessarily achieve acceptable performance in multi-level image thresholding, while some established algorithms are demonstrated to work better.
25.	Seyed Jalaleddin, Mousavirad, et al. (author) Human mental search : a new population-based metaheuristic optimization algorithm 2017 In: Applied intelligence (Boston). - : Springer Nature. - 0924-669X .- 1573-7497. ; 47:3, s. 850-887 Journal article (peer-reviewed)abstract Population-based metaheuristic algorithms have become popular in recent years with them getting used in different fields such as business, medicine, and agriculture. The present paper proposes a simple but efficient population-based metaheuristic algorithm called Human Mental Search (HMS). HMS algorithm mimics the exploration strategies of the bid space in online auctions. The three leading steps of HMS algorithm are: (1) the mental search that explores the region around each solution based on Levy flight, (2) grouping that determines a promising region, and (3) moving the solutions toward the best strategy. To evaluate the efficiency of HMS algorithm, some test functions with different characteristics are studied. The results are compared with nine state-of-the-art metaheuristic algorithms. Moreover, some nonparametric statistical methods, including Wilcoxon signed rank test and Friedman test, are provided. The experimental results demonstrate that the HMS algorithm can present competitive results compared to other algorithms.
26.	Seyed Jalaleddin, Mousavirad, et al. (author) Human mental search-based multilevel thresholding for image segmentation 2020 In: Applied Soft Computing. - : Elsevier BV. - 1568-4946 .- 1872-9681. ; 97 Journal article (peer-reviewed)abstract Multilevel thresholding is one of the principal methods of image segmentation. These methods enjoy image histogram for segmentation. The quality of segmentation depends on the value of the selected thresholds. Since an exhaustive search is made for finding the optimum value of the objective function, the conventional methods of multilevel thresholding are time-consuming computationally, especially when the number of thresholds increases. Use of evolutionary algorithms has attracted a lot of attention under such circumstances. Human mental search algorithm is a population-based evolutionary algorithm inspired by the manner of human mental search in online auctions. This algorithm has three interesting operators: (1) clustering for finding the promising areas, (2) mental search for exploring the surrounding of every solution using Levy distribution, and (3) moving the solutions toward the promising area. In the present study, multilevel thresholding is proposed for image segmentation using human mental search algorithm. Kapur (entropy) and Otsu (between-class variance) criteria were used for this purpose. The advantages of the proposed method are described using twelve images and in comparison with other existing approaches, including genetic algorithm, particle swarm optimization, differential evolution, firefly algorithm, bat algorithm, gravitational search algorithm, and teaching-learning-based optimization. The obtained results indicated that the proposed method is highly efficient in multilevel image thresholding in terms of objective function value, peak signal to noise, structural similarity index, feature similarity index, and the curse of dimensionality. In addition, two nonparametric statistical tests verified the efficiency of the proposed algorithm, statistically.
27.	Seyed Jalaleddin, Mousavirad, et al. (author) Metaheuristic-based energy-aware image compression for mobile app development 2024 In: Multimedia tools and applications. - : Springer. - 1380-7501 .- 1573-7721. Journal article (peer-reviewed)abstract The widely applied JPEG standard has undergone recent efforts using population-based metaheuristic (PBMH) algorithms to optimise quantisation tables (QTs) for specific images. However, user preferences, like an Android developer’s preference for small-size images, are often overlooked, leading to high-quality images with large file sizes. Another limitation is the lack of comprehensive coverage in current QTs, failing to accommodate all possible combinations of file size and quality. Therefore, this paper aims to propose three distinct contributions. First, to include the user’s opinion in the compression process, the file size of the output image can be controlled by a user in advance. To this end, we propose a novel objective function for population-based JPEG image compression. Second, we suggest a novel representation to tackle the lack of comprehensive coverage. Our proposed representation can not only provide more comprehensive coverage but also find the proper value for the quality factor for a specific image without any background knowledge. Both representation and objective function changes are independent of the search strategies and can be used with any population-based metaheuristic (PBMH) algorithm. Therefore, as the third contribution, we also provide a comprehensive benchmark on 22 state-of-the-art and recently-introduced PBMH algorithms on our new formulation of JPEG image compression. Our extensive experiments on different benchmark images and in terms of different criteria show that our novel formulation for JPEG image compression can work effectively.
28.	Seyed Jalaleddin, Mousavirad, et al. (author) Multilevel image thresholding using entropy of histogram and recently developed population-based metaheuristic algorithms 2017 In: Evolutionary Intelligence. - : Springer Nature. - 1864-5909 .- 1864-5917. ; 10:1-2, s. 45-75 Journal article (peer-reviewed)abstract Multilevel thresholding is one of the most broadly used approaches to image segmentation. However, the traditional techniques of multilevel thresholding are time-consuming, especially when the number of the threshold values is high. Thus, population-based metaheuristic (P-metaheuristic) algorithms can be used to overcome this limitation. P-metaheuristic algorithms are a type of optimization algorithms, which improve a set of solutions using an iterative process. For this purpose, image thresholding problem should be seen as an optimization problem. This paper proposes multilevel image thresholding for image segmentation using several recently presented P-metaheuristic algorithms, including whale optimization algorithm, grey wolf optimizer, cuckoo optimization algorithm, biogeography-based optimization, teaching–learning-based optimization, gravitational search algorithm, imperialist competitive algorithm, and cuckoo search. Kapur’s entropy is used as the objective function. To conduct a more comprehensive comparison, the mentioned P-metaheuristic algorithms were compared with five others. Several experiments were conducted on 12 benchmark images to compare the algorithms regarding objective function value, peak signal to noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), and stability. In addition, Friedman test and Wilcoxon signed rank test were carried out as the nonparametric statistical methods to compare P-metaheuristic algorithms. Eventually, to create a more reliable result, another objective function was evaluated based on Cross Entropy.
29.	Seyed Jalaleddin, Mousavirad, et al. (author) Population-based self-adaptive Generalised Masi Entropy for image segmentation : A novel representation 2022 In: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 245 Journal article (peer-reviewed)abstract Image segmentation is an indispensable part of computer vision applications, and image thresholding is a popular one due to its simplicity and robustness. Generalised Masi entropy (GME) is an image thresholding method that exploits the additive/non-extensive information using entropic measure (). shows the measure of degree of extensibility and non-extensibility available in an image. From the literature, all research considered it as a fixed coefficient, while finding a proper value for can enhance the efficacy of thresholding. This paper proposes a simple yet effective approach for adaptively finding a proper value for without any background knowledge regarding the distribution of histogram. To this end, a new representation is proposed so that it can be used with any type of population-based metaheuristic (PBMH) algorithms. For the optimisation process, we use differential evolution (DE), as a representative. In addition, to further improve efficacy, we improve DE algorithm based on one-step -means clustering, random-based sampling, Gaussian-based sampling, and opposition-based learning. Our extensive experiments compared to the most recent approaches on a set of benchmark images and in terms of several criteria clearly show that the proposed approach not only can find the proper value for automatically but also it can improve the efficacy of GME-based image thresholding methods.
30.	Zabihzadeh, Davood, et al. (author) Ensemble of loss functions to improve generalizability of deep metric learning methods 2023 In: Multimedia tools and applications. - 1380-7501 .- 1573-7721. Journal article (peer-reviewed)abstract The success of a Deep metric learning (DML) algorithm greatly depends on its loss function. However, no loss function is perfect and deals only with some aspects of an optimal similarity embedding. Besides, they omit the generalizability of the DML on unseen categories. To address these challenges, we propose novel approaches to combine different losses built on top of a shared deep network. The proposed ensemble of losses enforces the model to extract compatible features with all losses. Since the selected losses are diverse and emphasize different aspects of an optimal embedding, our effective combining method yields a considerable improvement over any individual loss and generalize well on unseen classes. It can optimize each loss function and its weight without imposing an additional hyper-parameter. We evaluate our methods on some popular datasets in a Zero-Shot-Learning setting. The results are very encouraging and show that our methods outperform all baseline losses by a large margin in all datasets. Specifically, the proposed method surpasses the best individual loss on the Cars-196 dataset by 10.37% and 9.54% in terms of Recall@1 and kNN accuracy respectively. Moreover, we develop a novel distance-based compression method that compresses the coefficient and embedding of losses into a single embedding vector. The size of the resulting embedding is identical to each baseline learner. Thus, it is fast as each baseline DML in the evaluation stage. Meanwhile, it outperforms the best individual loss on the Cars-196 dataset by 8.28% and 7.76% in terms of Recall@1 and kNN accuracy respectively.
31.	Zabihzadeh, Davood, et al. (author) Low-rank robust online distance/similarity learning based on the rescaled hinge loss 2022 In: Applied intelligence (Boston). - : Springer Nature. - 0924-669X .- 1573-7497. ; 53:1, s. 634-657 Journal article (peer-reviewed)abstract An important challenge in metric learning is scalability to both size and dimension of input data. Online metric learning algorithms are proposed to address this challenge. Existing methods are commonly based on Passive/Aggressive (PA) approach. Hence, they can rapidly process large volumes of data with an adaptive learning rate. However, these algorithms are based on the Hinge loss and so are not robust against outliers and label noise. We address the challenges by formulating the online Distance/Similarity learning problem with the robust Rescaled Hinge loss function. The proposed model is rather general and can be applied to any PA-based online Distance/Similarity algorithm. To achieve scalability to data dimension, we propose low-rank online Distance/Similarity methods that learn a rectangular projection matrix instead of a full Mahalanobis matrix. The low-rank approaches not only reduce the computational cost but also keep the discrimination power of the learned metrics. Also, current online methods usually assume training triplets or pairwise constraints exist in advance. However, this assumption does not hold, and generating triplets using available batch sampling methods is both time and space consuming. We address this issue by developing an efficient, yet effective robust one-pass triplet construction algorithm. We conduct several experiments on datasets from various applications. The results confirm that the proposed methods significantly outperform state-of-the-art online metric learning methods in the presence of label noise and outliers by a large margin.

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