Intrusion detection is the identification of unauthorized access of a computer network. This paper proposes a novel algorithm for a network intrusion detection system (NIDS) using an improved feature ...subset selected directly by a genetic algorithm (GA)-based exhaustive search and fuzzy C-means clustering (FCM). The algorithm identifies the bagging (BG) classifier and the convolutional neural network (CNN) model as an effective extractor by implementing the GA in combination with 5-fold cross validation (CV) to select the CNN model structure. The deep feature subset extracted by the selected CNN model is put into the BG classifier to validate the performance with the 5-fold CV. The high quality feature set obtained by the three-layered feature construction using the GA, FCM, CNN extractor, and a hybrid CNN and BG learning method significantly improves the final detection performance. Moreover, the highly reliable validation performance results achieved by the 5-fold CV procedure for the proposed algorithm imply a well-fitted application in a practical computer network environment NIDS.
•The feature quality is improved significantly by the three-layered feature construction including feature selection, feature improvement, and deep feature extraction. The GA is employed to select the most informative feature subset, which is processed by the Fuzzy C-means Clustering to compute additional features. The GA is also used to select the best CNN structures using features selected by the feature selection and feature improvement. The selected CNN models, known as the extractors, then generate deep features, which are put into the BG classifier.•The final detection performance of the proposed algorithm is relatively high by the utility of the hybrid learning method of the CNN and BG classifier.•Avoidance of the overfitting problem and better reliable performance results are obtained by implementation of the 5-folds cross validation procedure for feature selection, CNN model selection, and validation of the development method in this work.
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Neuromorphic computing systems are biologically inspired approaches created from many highly connected neurons to model neuroscience theories and solve machine learning problems. They promise to ...drastically improve the efficiency of critical computational tasks such as decision-making and perception. Combining neuromorphic computing systems and 3D interconnect (3D-NoC) technology leads to an advanced architecture that inherits the benefits of both computing and interconnect paradigms. However, designing large-scale neuromorphic systems based on 3D-NoC faces several challenges, including thermal power, power distribution, increased power density at the heat sink interface, and fabrication requirements. This work tackles the thermal issues in designing large-scale neuromorphic systems by proposing a Balanced Thermal-State-Aware Mapping (BTSAM) for 3D-NoC-based neuromorphic systems. This includes a Periodic Activity Scoring (PAS), a Seesaw Neuron Clustering (SNC) method, and a thermal-aware genetic algorithm to eliminate hotspots, balance the thermal state, and lower the temperature while keeping the system's accuracy acceptable. Evaluation results on various system configurations demonstrate a notable up to 12.4 K and 5.2 K temperature reduction compared to linear methods and HeterGenMap, respectively, and a 4× increase in Mean-Time-to-Failure (MTTF), with an acceptable power and area overheads and little degradation of the communication cost.
The constraints on the battery resources of sensor nodes have been the major stumbling block in achieving the network longevity and in exploring the potential of Wireless Sensor Network (WSN) to the ...maximum level. A plethora of research work has implemented multitudinous optimization techniques for the Cluster Head (CH) selection in homogenous WSN. However, for Heterogeneous WSN (HWSN), the CH selection is still left with a wide scope for further improvement for its exploitation capabilities. In this paper, Genetic Algorithm-based Optimized Clustering (GAOC) protocol is designed for optimized CH selection by integrating the parameters of residual energy, distance to the sink and node density in its formulated fitness function. Furthermore, to pact with the Hot-Spot problem, and to shorten the communicating distance from the nodes to the sink, Multiple data Sinks based GAOC (MS-GAOC) is proposed. The empirical investigations of MS-GAOC is carried out with protocols developed to operate with multiple data sinks so as to have fair comparative analysis. It is inferred from the simulation analysis that the GAOC and MS-GAOC outperform the state-of-the-art protocols on the benchmark of different performance metrics viz. stability period, network lifetime, number of dead nodes against rounds, throughput and network’s remaining energy. The proposed protocols are expected to play a salient role in monitoring of hostile applications, i.e., forest fire detection, early detection of volcanic eruptions, etc.
•A Genetic Algorithm-based Optimized Clustering (GAOC) protocol is proposed for HWSN.•To mitigate the Hot-Spot problem, Multiple data sinks based GAOC (MS-GAOC) protocol is proposed.•The Performance evaluation of GAOC is done against GADA-LEACH, DCHGA, and TEDRP protocols.•Multiple data sinks based protocols MS-GADA, MS-DCHGA, and MS-TEDRP are designed for comparison with MS-GAOC.•MS-GAOC is developed for unattended hostile applications viz. forest fire detection, etc.
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•An intelligent modelling framework is proposed for mechanical properties of CPB.•This framework combines machine learning (ML) algorithms and genetic algorithm.•1077 UCS tests and ...231 UTS tests were conducted to prepare dataset.•The performance of three advanced ML algorithms was verified and compared.•A software, the IMB, was developed for a wider application of this framework.
The mechanical properties of cemented paste backfill (CPB) are particularly important for its application in the minerals industry. In practice, a large number of cumbersome and time-consuming experiments are required to generate the design data. To facilitate the CPB design, this study proposes an intelligent modelling framework for the mechanical properties prediction using machine learning (ML) algorithms and genetic algorithm (GA). Three advanced ML algorithms, including decision tree (DT), gradient boosting machine (GBM), and random forest (RF), were used and compared for the mechanical properties modelling while GA was used for the hyper-parameters tuning. A total of 1077 uniaxial compressive strength (UCS) tests and 231 uniaxial tensile strength (UTS) tests were performed for the dataset preparation. Mechanical properties evaluated were the UCS, the yield strength (YS), the Young’s modulus (E) and the UTS. Influencing variables for these mechanical properties were chosen to be the physical and chemical characteristics of tailings, the cement-tailings ratio, the solids content, and the curing time. The results show that GA was efficient in the hyper-parameters tuning of the evaluated ML algorithms. The GBM was a good first ML algorithm for the mechanical properties modelling with high accuracy (correlation coefficients between predicted and experimental properties were 0.963, 0.887, 0.866 and 0.899 for UCS, YS, E and UTS respectively). Based on the results, a user-friendly software package, named the intelligent mining for backfill (IMB), was developed in python programming for a wider application in the minerals industry. The proposed modelling framework and the IMB will be useful for CPB design by saving time, reducing trial tests and cutting costs.
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This study develops a mathematical model to mitigate disruptions in a three-stage (i.e., supplier, manufacturer, retailer) supply chain network subject to a natural disaster like COVID-19 pandemic. ...This optimization model aims to manage supply chain disruptions for a pandemic situation where disruptions can occur to both the supplier and the retailer. This study proposes an inventory policy using the renewal reward theory for maximizing profit for the manufacturer under study. Tested using two heuristics algorithms, namely the genetic algorithm (GA) and pattern search (PS), the proposed inventory-based disruption risk mitigation model provides the manufacturer with an optimum decision to maximize profits in a production cycle. A sensitivity analysis was offered to ensure the applicability of the model in practical settings. Results reveal that the PS algorithm performed better for such model than a heuristic method like GA. The ordering quantity and reordering point were also lower in PS than GA. Overall, it was evident that PS is more suited for this problem. Supply chain managers need to employ appropriate inventory policies to deal with several uncertain conditions, for example, uncertainties arising due to the COVID-19 pandemic. This model can help managers establish and redesign an inventory policy to maximize the profit by considering probable disruptions in the supply chain network.
This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and ...wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at
http://www.alimirjalili.com/MVO.html
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, ODKLJ, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
•Proposed new definition of interval order relations.•Proposed new definition of interval metric.•Theoretical development of multi-objective optimization in interval domain.•Developed Tournament ...Genetic Algorithm for solving multi-objective optimization problem in interval domain.
The aim of this paper is to discuss the optimality of interval multi-objective optimization problems with the help of different interval metric. For this purpose, we have proposed the new definitions of interval order relations by modifying the existing definitions and also modified different definitions of interval mathematics. Using the definitions of interval order relations and interval metric, the multi-objective optimization problem is converted into single objective optimization problem by different techniques. Then the corresponding problems have been solved by hybrid Tournament Genetic Algorithm with whole arithmetic crossover and double mutation (combination of non-uniform and boundary mutations). To illustrate the methodology, five numerical examples have been solved and the computational results have been compared. Finally, to test the efficiency of the proposed hybrid Tournament Genetic Algorithm, sensitivity analyses have been carried out graphically with respect to genetic algorithm parameters.
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•Porous materials discovery with machine learning and genetic algorithm.•Manufacturing recipe explored to improve permeability and filtration efficiency.•Evolutionary history-based use of reduced ...design space for material performance.
This study proposes a material discovery framework for porous materials to identify design variable recipes and the corresponding material structures that can be utilized to improve the actual manufacturing process. The effectiveness of the proposed framework has been demonstrated via multi-objective genetic algorithm optimization with regard to permeability and filtration efficiency. A simulation model to generate porous material structures with two layers has been developed with design variables, such as grain diameter, grain shape, and ratio of pore former to base grain. The design variables have been optimized to maximize two objective functions, that is, permeability and filtration efficiency, which have been evaluated by machine-learning-based surrogate models with negligible computational cost as compared to the computational fluid dynamics (CFD) simulations. The surrogate models are updated once or regularly using the generated structures to improve the exploration capability according to the necessity of the optimization process. The proposed framework successfully unveiled design variable recipes and guidelines for obtaining preferable structures with high permeability and filtration efficiency in the actual manufacturing process.
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•A hybrid artificial intelligence model is proposed for predicting coal strength alteration induced by CO2 adsorption.•BPNN, GA and AdaBoost algorithms are combined.•The ...GA-BPNN-AdaBoost model has good prediction accuracy and generalization ability.•The MIV is used to investigate the relative importance of each input variable.
CO2 geological sequestration in coal seams has gradually become one of the effective means to deal with the global greenhouse effect. However, the injection of CO2 into the coal seam can have an important impact on the physical and chemical properties of coal, which in turn affects the CO2 sequestration performance in coal seams and causes a large number of environmental problems. In order to better evaluate the strength alteration of coal in CO2 geological sequestration, a hybrid artificial intelligence model integrating back propagation neural network (BPNN), genetic algorithm (GA) and adaptive boosting algorithm (AdaBoost) is proposed. A total of 112 data samples for unconfined compressive strength (UCS) are retrieved from the reported studies to train and verify the proposed model. The input variables for the predictive model include coal rank, CO2 interaction time, CO2 interaction temperature and CO2 saturation pressure, and the corresponding output variable is the measured UCS. The predictive model performance is evaluated by correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The predictive results denote that the GA-BPNN-AdaBoost predictive model is an efficient and accurate method to predict coal strength alteration induced by CO2 adsorption. The simultaneous optimization of BPNN by GA and AdaBoost algorithm can greatly improve the prediction accuracy and generalization ability of the model. At the same time, the mean impact value (MIV) is used to investigate the relative importance of each input variable. The relative importance scores of coal rank, CO2 interaction time, CO2 interaction temperature and CO2 saturation pressure are 0.5475, 0.2822, 0.0373, 0.1330, respectively. The research results in this paper can provide important guiding significance for CO2 geological sequestration in coal seams.
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Energy storage system can be used to increase the fuel economy of fuel cell system (FCS). In this study, a new method was introduced for optimizing the energy management strategy (EMS) for fuel cell ...vehicle (FCV) to reduce fuel consumption. The membership function (MF) in fuzzy control is subjective; thus, 12 design variables in the input‐output MFs were selected using sensitivity analysis, and elliptical basis function neural network method was used to establish a high‐precision approximate model of FCV. Multi‐island genetic algorithm was used to optimize the MFs. The effectiveness of the optimized fuzzy control EMS and the proposed optimization method were demonstrated in simulations of two EMSs under four driving cycles. The simulation results confirmed that the optimized fuzzy control EMS provided smoother and more stable output power from FCS reducing hydrogen consumption by 8.4%, 1.1%, 5.1%, and 7.6%, respectively, compared to the original fuzzy control EMS; and hydrogen saved by the optimized EMS provided extra range of 9.15, 1.10, 5.37, and 8.25 km per 100 km in the four driving cycles, respectively. The optimized EMS can reduce hydrogen consumption to increase fuel economy and extend the life span of the fuel cell.
A method to reduce fuel consumption by optimizing the energy management strategy of fuel cell vehicles is introduced. Optimized EMS can improve economic characteristics by reducing hydrogen consumption and extend the service life of fuel cells.
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