Combination Of Evolutionary Algorithm Research Articles

Cable force optimization of cable-stayed bridges: A surrogate model-assisted differential evolution method combined with B-Spline interpolation curves

Cable-stayed bridges have been widely constructed to cross large obstacles in transportation networks and metropolitan cities. As a highly redundant structure, the structural performance of a cable-stayed bridge can be significantly influenced by the cable prestressing forces. Therefore, the determination of optimal cable forces is necessary at the design stage. As computational mechanics matures, numerical computational methods such as high-fidelity finite element analyses facilitate accurate assessment of the bridge state, and evolutionary algorithms have been applied to enhance the global search ability. However, the combination of evolutionary algorithms and finite element model-based evaluations can substantially increase the computational burden, rendering the efficiency of optimization significantly time-consuming for practical engineering projects. To overcome this limitation, this work proposes a new surrogate model-assisted differential evolution method to facilitate the determination of optimal cable forces. Different from the existing methods, the proposed method develops a new individual selection strategy together with an additional archive to manage the diversity of population and further enhance the exploration ability. A new criterion is also proposed for the refinement of the surrogate model in the local search stage to avoid redundant information. Furthermore, B-Spline interpolation curve is integrated within the framework as a high-dimensional model representation technique for the design variables of cable forces. Five classic numerical examples and a practical engineering application validate the performance and advantages of the proposed method.

Industrial IoT Intrusion Detection via Evolutionary Cost-Sensitive Learning and Fog Computing

Cyber attacks and intrusions have become the major obstacles to the adoption of the Industrial Internet of Things (IIoT) in critical industries. Imbalanced data distribution is a common problem in IIoT environments that negatively influence machine learning-based intrusion detection systems (IDSs). To address this issue, we introduce EvolCostDeep, a hybrid model of stacked autoencoders (SAE) and convolutional neural networks (CNNs) with a new cost-dependent loss function. The loss function aims to optimize the model’s parameters, where the costs are determined using an evolutionary algorithm. The combination of evolutionary algorithms and deep learning (DL) on Big data hinders the scalability of IIoT IDSs. In this regard, a fog computing-enabled framework, called DeepIDSFog, is designed at the data level, where the master node shares the EvolCostDeep model with worker nodes. In each fog worker node, the EvolCostDeep is parallelized through one task-level and two model-level mechanisms. After aggregating detection outputs from worker nodes to the master, the result is passed to the cloud platform for mitigating attacks. A series of experiments is conducted on the ToN-IoT and UNSW-NB15 data sets to evaluate the performance of EvolCostDeep and DeepIDSFog. The results show that our frameworks can effectively handle both class imbalance problem and scalability of big IIoT traffic data compared with the other models. The averaged values of the EvolCostDeep for recall, precision, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> -score on the data sets are of 93.3%, 97.6%, and 95.2%, respectively, which are higher than the compared methods. Also, the DeepIDSFog provides an average speedup of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$38.7\times $ </tex-math></inline-formula> over other comparing models.

Computational discovery of two-dimensional copper chalcogenidesCuX(X= S, Se, Te)

We investigate the energy landscape of 2D CuS, CuSe, and CuTe compounds using a global evolutionary algorithm in combination with density functional theory. Four low-lying energy ${\mathrm{Cu}}_{2}{X}_{2}$ (X=S, Se, Te) slabs with $P\overline{3}m1$, $P4/mmm$, $P4/nmm$, and $Pmmn$ symmetries have been identified on their respective potential energy surfaces. Three structures present tetrahedral ${\mathrm{Cu}X}_{4}$ motifs which pave the 2D slabs, while the latter is based on shared ${\mathrm{Cu}}_{4}{X}_{2}$ octahedra. The viability of each phase was examined by looking at dynamical, thermodynamic, and thermal stability criteria. We find that 2D copper monosulfide crystallizes in the $P\overline{3}m1$ phase, reminiscent of the covalent slab found in the $\mathrm{Ca}{\mathrm{Al}}_{2}{\mathrm{Si}}_{2}$ prototype. Two polymorphs of 2D copper selenide with symmetries $P\overline{3}m1$ and $P4/mmm$ are close in energy. The latter ${\mathrm{Cu}}_{2}{\mathrm{Se}}_{2}$ slab is made of fused ${\mathrm{Cu}}_{4}{\mathrm{Se}}_{2}$ square bipyramids with electronegative Se atoms in apical positions. Finally, the ground-state 2D CuTe has a $Pmmn$ space group containing distorted ${\mathrm{CuTe}}_{4}$ tetrahedra with some Te--Te bonding. The electronic and bond analyses show that all of these predicted 2D CuX phases are metallic with ionocovalent bonds.

Performance Improvement in Brain Tumor Detection in MRI Images Using a Combination of Evolutionary Algorithms and Active Contour Method.

The process of treating brain cancer depends on the experience and knowledge of the physician, which may be associated with eye errors or may vary from person to person. For this reason, it is important to utilize an automatic tumor detection algorithm to assist radiologists and physicians for brain tumor diagnosis. The aim of the present study is to automatically detect the location of the tumor in a brain MRI image with high accuracy. For this end, in the proposed algorithm, first, the skull is separated from the brain using morphological operators. The image is then segmented by six evolutionary algorithms, i.e., Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Differential Evolution (DE), Harmony Search (HS), and Gray Wolf Optimization (GWO), as well as two other frequently-used techniques in the literature, i.e., K-means and Otsu thresholding algorithms. Afterwards, the tumor area is isolated from the brain using the four features extracted from the main tumor. Evaluation of the segmented area revealed that the PSO has the best performance compared with the other approaches. The segmented results of the PSO are then used as the initial curve for the Active contour to precisely specify the tumor boundaries. The proposed algorithm is applied on fifty images with two different types of tumors. Experimental results on T1-weighted brain MRI images show a better performance of the proposed algorithm compared to other evolutionary algorithms, K-means, and Otsu thresholding methods.

The route problem of multimodal transportation with timetable: stochastic multi-objective optimization model and data-driven simheuristic approach

PurposeDistribution of long-haul goods could be managed via multimodal transportation networks where decision-maker has to consider these factors including the uncertainty of transportation time and cost, the timetable limitation of selected modes and the storage cost incurred in advance or delay arriving of the goods. Considering the above factors comprehensively, this paper establishes a multimodal multi-objective route optimization model which aims to minimize total transportation duration and cost. This study could be used as a reference for decision-maker to transportation plans.Design/methodology/approachMonte Carlo (MC) simulation is introduced to deal with transportation uncertainty and the NSGA-II algorithm with an external archival elite retention strategy is designed. An efficient transformation method based on data-drive to overcome the high time-consuming problem brought by MC simulation. Other contribution of this study is developed a scheme risk assessment method for the non-absolutely optimal Pareto frontier solution set obtained by the NSGA-II algorithm.FindingsNumerical examples verify the effectiveness of the proposed algorithm as it is able to find a high-quality solution and the risk assessment method proposed in this paper can provide support for the route decision.Originality/valueThe impact of timetable on transportation duration is analyzed and making a detailed description in the mathematical model. The uncertain transportation duration and cost are represented by random number that obeys a certain distribution and designed NSGA-II with MC simulation to solve the proposed problem. The data-driven strategy is adopted to reduce the computational time caused by the combination of evolutionary algorithm and MC simulation. The elite retention strategy with external archiving is created to improve the quality of solutions. A risk assessment approach is proposed for the solution scheme and in the numerical simulation experiment.

A neuro-genetic Hopfield associative memory (HAM) with improved noise immunity

Artificial Neural Network (ANN) is a form of computation inspired by the arrangement and purpose of the brain. Pattern Recognition is one of the demanding areas under Artificial Neural Network and pattern association is one of the major tasks through which pattern recognition is realized. Nowadays researchers are taking interest in the combination of evolutionary algorithms and ANN. In the present work, an attempt is being made to analyze the performance of Hopfield Associative Memory with Monte Carlo adaptation rule and evolutionary searching (Genetic Algorithm) for pattern storage and recall. The training pattern set has been taken a set of five Greek alphabet letters as different objects. The aim is to store Patterns with MC adaptation rule and get the best weight matrices for efficient recalling of any given set patterns. The neural networks architectures have been taken up for storing the given set of inputs, for which conventional Hebbian and Monte Carlo adaptation rule is considered. During the mutation process, various mutation probabilities are supposed to be evaluated. The detailed simulated results thus obtained will be presented with the help of tables and graph.

Predicting the settlement of geosynthetic-reinforced soil foundations using evolutionary artificial intelligence technique

In order to ensure safe and sustainable design of geosynthetic-reinforced soil foundation (GRSF), settlement prediction is a challenging task for practising civil/geotechnical engineers. In this paper, a new hybrid technique for predicting the settlement of GRSF has been proposed based on the combination of evolutionary algorithm, that is, grey-wolf optimisation (GWO) and artificial neural network (ANN), abbreviated as ANN-GWO model. For this purpose, the reliable pertinent data were generated through numerical simulations conducted on validated large-scale 3-D finite element model. The predictive power of the model was assessed using various well-established statistical indices, and also validated against several independent scientific studies as reported in literature. Furthermore, the sensitivity analysis was conducted to examine the robustness and reliability of the model. The results as obtained have indicated that the developed hybrid ANN-GWO model can estimate the maximum settlement of GRSF under service loads in a reliable and intelligent way, and thus, can be deployed as a predictive tool for the preliminary design of GRSF. Finally, the model was translated into functional relationship which can be executed without the need of any expensive computer-based program.

Numerical analysis of the Volterra differential equations via a combination of evolutionary algorithms with moving least squares and finite difference methods

In this paper, the Volterra differential equations governing on the predator-prey dynamics is numerically solved through the combination of evolutionary algorithms with Moving Least Squares as well as the Finite Difference Method. Among the numerous evolutionary algorithms, the Genetic Algorithms and Particle Swarm Optimization, as two powerful and effective approaches, are selected. The penalty method is chosen to impose the initial conditions on the objective function, while the Moving Least Squares is implemented to interpolate the derivatives in the differential equations. In order to illustrate the effectiveness of the Moving Least Squares, the results are compared with those obtained via the Finite Difference Method based evolutionary algorithms. Moreover, the results are associated with those of a numerical method and an analytical scheme to evaluate the obtained populations of the prey and the predator. These comparisons clearly illustrate the reliability and efficiency of the proposed methodology to solve nonlinear differential equations.

Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach.

Knee osteoarthritis (KOA) is a multifactorial disease which is responsible for more than 80% of the osteoarthritis disease’s total burden. KOA is heterogeneous in terms of rates of progression with several different phenotypes and a large number of risk factors, which often interact with each other. A number of modifiable and non-modifiable systemic and mechanical parameters along with comorbidities as well as pain-related factors contribute to the development of KOA. Although models exist to predict the onset of the disease or discriminate between asymptotic and OA patients, there are just a few studies in the recent literature that focused on the identification of risk factors associated with KOA progression. This paper contributes to the identification of risk factors for KOA progression via a robust feature selection (FS) methodology that overcomes two crucial challenges: (i) the observed high dimensionality and heterogeneity of the available data that are obtained from the Osteoarthritis Initiative (OAI) database and (ii) a severe class imbalance problem posed by the fact that the KOA progressors class is significantly smaller than the non-progressors’ class. The proposed feature selection methodology relies on a combination of evolutionary algorithms and machine learning (ML) models, leading to the selection of a relatively small feature subset of 35 risk factors that generalizes well on the whole dataset (mean accuracy of 71.25%). We investigated the effectiveness of the proposed approach in a comparative analysis with well-known FS techniques with respect to metrics related to both prediction accuracy and generalization capability. The impact of the selected risk factors on the prediction output was further investigated using SHapley Additive exPlanations (SHAP). The proposed FS methodology may contribute to the development of new, efficient risk stratification strategies and identification of risk phenotypes of each KOA patient to enable appropriate interventions.

Theoretical study of ternary silver fluorides AgMF4 (M = Cu, Ni, Co) formation at pressures up to 20 GPa.

Only several compounds bearing the Ag(ii) cation and other paramagnetic transition metal cations are known experimentally. Herein, we predict in silico stability and crystal structures of hypothetical ternary silver(ii) fluorides with copper, nickel and cobalt in 1 : 1 stoichiometry at a pressure range from 0 GPa up to 20 GPa employing the evolutionary algorithm in combination with DFT calculations. The calculations show that AgCoF4 could be synthesized already at ambient conditions but this compound would host diamagnetic Ag(i) and high-spin Co(iii). Although none of the compounds bearing Ag(ii) could be preferred over binary substrates at ambient conditions, at increased pressure ternary fluorides of Ag(ii) featuring Cu(ii) and Ni(ii) could be synthesized, in the pressure windows of 7–14 and 8–15 GPa, respectively. All title compounds would be semiconducting and demonstrate magnetic ordering. Compounds featuring Ni(ii) and particularly Co(ii) should exhibit fundamental band gaps much reduced with respect to pristine AgF2. The presence of Cu(ii) and Ni(ii) does not lead to electronic doping to AgF2 layers, while Co(ii) tends to reduce Ag(ii) entirely to Ag(i).

Evolutionary computation and genetic algorithms for designing the optimal mine haul road pavements

An overview of the software for designing flexible road pavements used in the USA, European countries and the Russian Federation is given, conceptual and mathematical models for designing flexible road pavements with the minimum cost per a linear meter are presented, the application of evolutionary algorithms in combination with exhaustive search and parallel computing is substantiated, the developed hybrid genetic algorithm for the optimization of flexible road pavement designs in terms of value of materials of layers is described, the results of a simulation experiment performed using the software developed within the framework of the study are presented in the article. It is proposed to use the developed software and algorithms for designing the optimal haul road pavements in terms of material cost.

A novel local search method for LSGO with golden ratio and dynamic search step

Depending on the developing technology, large-scale problems have emerged in many areas such as business, science, and engineering. Therefore, large-scale optimization problems and solution techniques have become an important research field. One of the most effective methods used in this research field is memetic algorithm which is the combination of evolutionary algorithms and local search methods. The local search method is an important part that greatly affects the memetic algorithm’s performance. In this paper, a novel local search method which can be used in memetic algorithms is proposed. This local search method is named as golden ratio guided local search with dynamic step size (GRGLS). To evaluate the performance of proposed local search method, two different performance evaluations were performed. In the first evaluation, memetic success history-based adaptive differential evolution with linear population size reduction and semi-parameter adaptation (MLSHADE-SPA) was chosen as the main framework and comparison is made between three local search methods which are GRGLS, multiple trajectory search local search (MTS-LS1) and modified multiple trajectory search. In the second evaluation, the improved MLSHADE-SPA (IMLSHADE-SPA) framework which is a combination of MLSHADE-SPA framework and proposed local search method (GRGLS) was compared with some recently proposed nine algorithms. Both of the experiments were performed using CEC’2013 benchmark set designed for large-scale global optimization. In general terms, the proposed method achieves good results in all functions, but it performs superior on overlapping and non-separable functions.

New hybrid method for attack detection using combination of evolutionary algorithms, SVM, and ANN

Intrusion detection systems (IDS) have been playing an important role for providing security of computer networks. They detect different types of attacks and malicious software usage, which sometimes cannot be identified by firewalls. Based on machine learning algorithms, many IDS have been extended to classify network traffic as normal or abnormal. This paper describes a new hybrid intrusion detection method with two phases - a feature selection phase and an attack detection phase. In the feature selection phase, a wrapper technique, namely MGA-SVM, is used. This technique combines features of support vector machine (SVM) and the genetic algorithm with multi-parent crossover and multi-parent mutation (MGA). In the attack detection phase, an artificial neural network (ANN) is used to detect attacks. For improving its performance, a combination of a hybrid gravitational search (HGS) and a particle swarm optimization (PSO) is used to train the classifier. The proposed hybrid method is thus called MGA-SVM-HGS-PSO-ANN. It's performance is compared with other popular techniques such as Chi-SVM, ANN based on gradient descent (GD-ANN) and decision tree (DT), ANN based on genetic algorithm (GA-ANN), ANN based on combining gravitational search (GS) and PSO (GSPSO-ANN), ANN based on PSO (PSO-ANN), and ANN based on GS (GS-ANN). Using the NSL-KDD dataset as a standard benchmark for attack detection evaluation, the obtained test results show that the proposed MGA-SVM-HGS-PSO-ANN method can attain a maximum detection accuracy of 99.3%, dimension reduction of NSL-KDD from 42 to 4 features, and needs only 3 s as maximum training time.

Efficient meta-heuristic approaches in solving minimal exposure path problem for heterogeneous wireless multimedia sensor networks in internet of things

One of the well-known methods for evaluating Heterogeneous wireless multimedia sensor networks (HWMSNs) in Internet of Things have drawn attention of the research community because this type of networks possesses great advantages of both coverage and performance. One of the most fundamental issues in HWMSNs is the barrier coverage problem which evaluates the surveillance capability of the network systems, especially those designed for security purposes. Among multiple approaches to solve this issue, finding the minimal exposure path (MEP), which corresponds to the worst-case coverage of the network is the most popular and efficient way. However, the MEP problem in HWMSNs (hereinafter heterogeneous multimedia MEP or HM-MEP) is specifically complex and challenging with the unique features of the HWMSNs. Thus, the problem is then converted into numerical functional extreme with high dimension, non-differential and non-linearity. Adapting to these features, two efficient meta-heuristic algorithms, Hybrid Evolutionary Algorithm (HEA) and Gravitation Particle Swarm Optimization (GPSO) are proposed for solving the problem. The HEA is a hybrid evolutionary algorithm in combination with local search while the GPSO is a novel particle swarm optimization based on the gravity force theory. Experimental results on extensive instances indicate that the proposed algorithms are suitable for the HM-MEP problem and perform well in term of both solution accuracy and computation time compared to existing approaches.

Applications of surrogate-assisted and multi-fidelity multi-objective optimization algorithms to simulation-based aerodynamic design

Purpose The purpose of this work is to apply and compare surrogate-assisted and multi-fidelity, multi-objective optimization (MOO) algorithms to simulation-based aerodynamic design exploration. Design/methodology/approach The three algorithms for multi-objective aerodynamic optimization compared in this work are the combination of evolutionary algorithms, design space reduction and surrogate models, the multi-fidelity point-by-point Pareto set identification and the multi-fidelity sequential domain patching (SDP) Pareto set identification. The algorithms are applied to three cases, namely, an analytical test case, the design of transonic airfoil shapes and the design of subsonic wing shapes, and are evaluated based on the resulting best possible trade-offs and the computational overhead. Findings The results show that all three algorithms yield comparable best possible trade-offs for all the test cases. For the aerodynamic test cases, the multi-fidelity Pareto set identification algorithms outperform the surrogate-assisted evolutionary algorithm by up to 50 per cent in terms of cost. Furthermore, the point-by-point algorithm is around 27 per cent more efficient than the SDP algorithm. Originality/value The novelty of this work includes the first applications of the SDP algorithm to multi-fidelity aerodynamic design exploration, the first comparison of these multi-fidelity MOO algorithms and new results of a complex simulation-based multi-objective aerodynamic design of subsonic wing shapes involving two conflicting criteria, several nonlinear constraints and over ten design variables.

Image-based emotion recognition using evolutionary algorithms

Abstract In pattern recognition, the classification accuracy has a strong correlation with the selected features. Therefore, in the present paper, we applied an evolutionary algorithm in combination with linear discriminant analysis (LDA) to enhance the feature selection in a static image-based facial expressions system. The accuracy of the classification depends on whether the features are well representing the expression or not. Therefore the optimization of the selected features will automatically improve the classification accuracy. The proposed method not only improves the classification but also reduces the dimensionality of features. Our approach outperforms linear-based dimensionality reduction algorithms and other existing genetic-based feature selection algorithms. Further, we compare our approach with VGG (Visual Geometry Group)-face convolutional neural network (CNN), according to the experimental results, the overall accuracy is 98.67% for either our approach or VGG-face. However, the proposed method outperforms CNN in terms of training time and features size. The proposed method proves that it is able to achieve high accuracy by using far fewer features than CNN and within a reasonable training time.

Computer predictions on Rh-based double perovskites with unusual electronic and magnetic properties

In search for new magnetic materials, we make computer prediction of structural, electronic and magnetic properties of yet-to-be synthesized Rh-based double perovskite compounds, Sr(Ca)2BRhO6 (B=Cr, Mn, Fe). We use combination of evolutionary algorithm, density functional theory, and statistical–mechanical tool for this purpose. We find that the unusual valence of Rh5+ may be stabilized in these compounds through formation of oxygen ligand hole. Interestingly, while the Cr–Rh and Mn–Rh compounds are predicted to be ferromagnetic half-metals, the Fe–Rh compounds are found to be rare examples of antiferromagnetic and metallic transition-metal oxide with three-dimensional electronic structure. The computed magnetic transition temperatures of the predicted compounds, obtained from finite temperature Monte Carlo study of the first principles-derived model Hamiltonian, are found to be reasonably high. The prediction of favorable growth condition of the compounds, reported in our study, obtained through extensive thermodynamic analysis should be useful for future synthesize of this interesting class of materials with intriguing properties.

Evolutionary design of the memory subsystem

The memory hierarchy has a high impact on the performance and power consumption in the system. Moreover, current embedded systems, included in mobile devices, are specifically designed to run multimedia applications, which are memory intensive. This increases the pressure on the memory subsystem and affects the performance and energy consumption. In this regard, the thermal problems, performance degradation and high energy consumption, can cause irreversible damage to the devices.We address the optimization of the whole memory subsystem with three approaches integrated as a single methodology. Firstly, the thermal impact of register file is analyzed and optimized. Secondly, the cache memory is addressed by optimizing cache configuration according to running applications and improving both performance and power consumption. Finally, we simplify the design and evaluation process of general-purpose and customized dynamic memory manager, in the main memory. To this aim, we apply different evolutionary algorithms in combination with memory simulators and profiling tools. This way, we are able to evaluate the quality of each candidate solution and take advantage of the exploration of solutions given by the optimization algorithm. We also provide an experimental experience where our proposal is assessed using well-known benchmark applications.

Combined Computational and in Situ Experimental Search for Phases in an Open Ternary system, Ba–Ru–S

Rapid materials discovery in inorganic chemistry should combine predictive computational tools with fast experimental syntheses. We apply such a tandem approach to explore the Ba–Ru–S phase space, where no ternary compounds are yet known to exist. Related ternary oxide ruthenates and ternary iron sulfides exhibit interesting electronic properties due to d-electron correlations, such as superconductivity, metamagnetism, and quantum phase transitions. We use a combination of evolutionary algorithms and density functional theory to inform traditional and in situ diffraction methods. In the course of our investigation, we find that convex hull constructions of the binary constituents inform interpretation of the ternary hull, which in this case has two compounds near thermodynamic stability. Our experimental study does not reveal formation of the candidates BaRu2S2 or BaRuS3, but it does provide the structure of a high-temperature polymorph of BaS2. This methodology can be exploited to study other ternary sys...

Identification of extreme wave heights with an evolutionary algorithm in combination with a likelihood-based segmentation

This paper presents four configurations of a genetic algorithm (GA) combined with a local search (LS) method for time series segmentation with the purpose of correctly recognising extreme values. The LS method is based on likelihood maximisation of a beta distribution. The proposal is tested on three real ocean wave height time series, where extreme values are frequently found. Concretely, the time series are taken from two oceanographic buoys in the Gulf of Alaska, and another one from Puerto Rico. The results show that the different combinations of LS improve the results of the GA. Furthermore, the algorithm provides segmentations where extreme values are grouped in a well-defined cluster, which allows the study of the characteristics of this type of events.