A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience ...of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon.
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IZUM, KILJ, NUK, PILJ, SAZU, UL, UM, UPUK
This paper focuses on the state of charge (SOC) estimation of a lithium-ion battery based on a fractional-order adaptive extended Kalman filter (FO-AEKF). First, a fractional order model (FOM) is ...introduced to describe the physical behavior of the battery which is superior than the integral-order model (IOM), because there are diffused and decentralized characteristics in battery inner parameters. Then, the parameters of the FOM are identified by a genetic algorithm which can realize optimal parameter identification. After that, the FO-AEKF algorithm is developed, which combines the advantages of the FOM and the adaptive strategy. Consequently, the FO-AEKF can quickly track the unknown and time-invariant (or slow time-varying) noise variance and then improve the accuracy of SOC estimation. Finally, two types of lithium-ion batteries and two dynamic operation conditions are given to show the efficiency of the FO-AEKF by comparing with the extended Kalman filter (EKF) for FOM and the adaptive extended Kalman filter (AEKF) for IOM.
•An adaptive extended kalman filter based on fractional order model is proposed.•This method is self-adaptive to unknown and time-invariant (or slow time-varying) noise.•Fractional order model is used to describe the dynamic characteristics of batteries.•Genetic algorithm is applied to indentify parameters of batteries.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•An adaptive discrete grey model is proposed for long-term photovoltaic power generation forecasting.•The proposed model can grasp nonlinear, fluctuant, and periodic patterns in various datasets.•The ...genetic algorithm is utilized to determine the adaptive parameters.•The proposed method strikingly outperforms a range of prevalent benchmarks.
The rapidly growing photovoltaic power generation (PPG) instigates stochastic volatility of electricity supply that may compromise the power grid’s stability and increase the grid imbalance cost. Therefore, accurate predictions of long-term PPG are of essential importance for the capacity deployment, plan improvement, consumption enhancement, and grid balance in systems with high penetration levels of PPG. Artificial neuron networks (ANNs) have been widely utilized to forecast the short-term PPG due to their strong nonlinear fitting competence that corresponds to the prerequisite for handling PPG samples characterized by volatility and nonlinearity. However, under the circumstances of the large time span, the insufficient data samples, and the periodicity existing in the long-term PPG datasets, the ANNs are easily stuck in overfitting and generate large forecasting deviations. Given this situation, a novel discrete grey model with time-varying parameters is initially designed to deal with various PPG time series featured with nonlinearity, periodicity, and volatility, which widely exist in the long-term PPG sequences. To be specific, improvements in this proposed model lie in the following aspects: first, the time-power item and periodic item are designated to compose the time-varying parameters to capture the nonlinear, periodic, and fluctuant developing trends of various time series. Second, owing to the complex nonlinear relationships between the above parameters and forecasting errors, the genetic algorithm applies shortcuts to seek optimum solutions and thereby enhances the prediction precision. Third, several practical properties of the proposed model are elaborated to further interpret the feasibility and adaptability of the proposed model. In experiments, a range of machine learning methods, autoregression models, and grey models are involved for comparisons to validate the feasibility and efficacy of the novel model, through the observations of the PPG in America and China. Finally, a superlative performance of the proposed model with the highest forecasting precision, small volatility of empirical results, and generalizability are confirmed by the aforementioned cases.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZRSKP
Availability of suitable and validated data is a key issue in multiple domains for implementing machine learning methods. Higher data dimensionality has adverse effects on the learning algorithm's ...performance. This work aims to design a method that preserves most of the unique information related to the data with minimum number of features. Addressing the feature selection problem in the domain of network security and intrusion detection, this work contributes an enhanced Genetic Algorithm (GA)-based feature selection method, named as GA-based Feature Selection (GbFS), to increase the classifiers’ accuracy. Securing a network from the cyber-attacks is a critical task and needs to be strengthened. Machine learning, due to its proven results, is widely used in developing firewalls and Intrusion Detection Systems (IDSs) to identify new kinds of attacks. Utilizing machine learning algorithms, IDSs are able to detect the intruder by analyzing the network traffic passing through it. This work presents parameter tuning for the GA-based feature selection along with a novel fitness function. The present work develops an enhanced GA-based feature selection method which is tested over three benchmark network traffic datasets, namely, CIRA-CIC-DOHBrw-2020, UNSW-NB15, and Bot-IoT. A comparison is also performed with the standard feature selection methods. Results show that the accuracies improve using GbFS by achieving a maximum accuracy of 99.80%.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Channel shape design has a significant effect on the performance of a proton exchange membrane fuel cell. Inspired by the fins of cuttlefish, a bio-inspired wave-like structure is designed and ...applied to the channel of fuel cells. The impact of this bio-inspired wave-like channel on fuel cell performance is investigated through a three-dimensional and non-isothermal model developed in COMSOL Multiphysics. The effects of channel center amplitude and number of wave cycles on the current density and pressure drop of fuel cells are studied. Compared with fuel cells with basic straight channel and conventional wave-like channel, the results show that fuel cell with this bio-inspired wave-like channel has high efficiency and low flow resistance, which can obtain better comprehensive performance. In addition, an optimization of the waveform for bio-inspired wave-like channel is performed by genetic algorithm in consideration of the output power and power consumption of flow. The optimal channel with a center amplitude of 0.305 mm and the number of wave cycles of 3.52 improves the output power density by 2.2%.
•A bio-inspired wave-like channel based on the fins of cuttlefish for PEMFC is introduced.•The bio-inspired channel enhances mass transfer for GDL, increases the current density.•The bio-inspired wave-like channel is proved to have high efficiency and low flow resistance.•Parameter optimization for the channel is performed by GA with two objective.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
India’s ever increasing population has made it necessary to develop alternative modes of transportation with electric vehicles being the most preferred option. The major obstacle is the deteriorating ...impact on the utility distribution system brought about by improper setup of these charging stations. This paper deals with the optimal planning (siting and sizing) of charging station infrastructure in the city of Allahabad, India. This city is one of the upcoming smart cities, where electric vehicle transportation pilot project is going on under Government of India initiative. In this context, a hybrid algorithm based on genetic algorithm and improved version of conventional particle swarm optimization is utilized for finding optimal placement of charging station in the Allahabad distribution system. The particle swarm optimization algorithm re-optimizes the received sub-optimal solution (site and the size of the station) which leads to an improvement in the algorithm functionality and enhances quality of solution. The genetic algorithm and improved version of conventional particle swarm optimization algorithm will also be compared with a conventional genetic algorithm and particle swarm optimization. Through simulation studies on a real time system of Allahabad city, the superior performance of the aforementioned technique with respect to genetic algorithm and particle swarm optimization in terms of improvement in voltage profile and quality.
•A novel optimal placement strategy of electric vehicles charging station.•A hybrid algorithm based on both GA and PSO.•Voltage profile shown improvement in the lowest p.u. voltage value.•A better performance in terms of quality of solution with lesser number of iterations.•The minimum stress on the distribution system.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Hybrid flow shop scheduling problems are encountered in many real-world manufacturing operations such as computer assembly, TFT-LCD module assembly, and solar cell manufacturing. Most research ...considers the scheduling problem in regard to time requirements and the steps needed to improve production efficiency. However, the increasing amount of carbon emissions worldwide is contributing to the worsening global warming problem. Many countries and international organizations have started to pay attention to this problem, even creating mechanisms to reduce carbon emissions. Furthermore, manufacturing enterprises are showing growing interest in realizing energy savings. Thus, the present research study focuses on reducing energy costs and completion time at the manufacturing-system level. This paper proposed a multi-objective mixed-integer programming for energy-efficient hybrid flow shop scheduling with lot streaming in order to minimize both the production makespan and electric power consumption. Due to a trade-off between these objectives and the computational complexity of the proposed multi-objective mixed-integer program, this study adopts the genetic algorithm (GA) to obtain approximate Pareto solutions more efficiently. In addition, a multi-objective energy efficiency scheduling algorithm is also developed to calculate the fitness values of each chromosome in GA.
•A multi-scale CAE network is used to extract features from three scale levels.•Only the data collected under health conditions are used for network updating.•A weighted relative similarity is ...proposed to construct health indicators.•Bearing and milling datasets are used to verify the performance of the proposed method.
In order to assess the degradation process of machines, it is necessary to construct a suitable health indicator. Existing health indicators are mainly constructed with manually extracted features. Those manually extracted features are based on the rich domain knowledge of experts. However, domain knowledge may be difficult to obtain. In order to automatically construct a health indicator, an unsupervised feature learning based health indicator construction method is proposed in this paper. The proposed method mainly consists of three steps: Firstly, a multiscale convolutional autoencoder network is built where the network hyperparameters are optimized through a genetic algorithm. Then, acquired sensor signals are directly input into the constructed network to adaptively learn features. For enhancing the effective features and suppressing the useless ones, different weights are assigned to all features. At last, the relative similarity of learned features between the baseline sample data and the currently acquired sample data is calculated as a health indicator to represent the health condition of machines. The effectiveness of the proposed method is validated through two cases. In those case studies, two metrics, including trendability and scale similarity, are used to quantitatively compare the performance of the proposed method with some other state-of-the-art ones. Results demonstrate that the health indicator constructed with the proposed method is able to effectively identify the degradation process of machines and obtain better performance than those comparative ones.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
There is an ongoing evolution involving a new approach to large-scale optimisations based on co-evolutionary searches using interacting heterogeneous agent-processes via the implementation of ...synchronised genetic algorithms with local populations. The individualisation of heuristic operators at the level of agent-processes that implement independent evolutionary searches facilitate the improved likelihood of obtaining the best solutions in the fastest time. Based on this property, a parallel multi-agent single-objective real-coded genetic algorithm for large-scale constrained black-box single-objective optimisations (LSOPs) is proposed. This facilitates the effective frequency exchange of the best potential decisions between interacting agent-processes with individual parameters, such as types of crossover and mutation operators with their own characteristics. We have improved the quality of both solutions and the time-efficiency of a multi-agent real-coded genetic algorithm (MA−RCGA). A novel framework was developed that represents the aggregation of MA−RCGA with simulation models by implementing a set of objective functions for real-world large-scale optimisation problems such as the simulation model of the ecological-economics system implemented in the AnyLogic tool.
•We developed a new multi-agent real-coded genetic algorithm (MA-RCGA).•There are investigated different performance characteristics of MA-RCGA.•MA-RCGA has been compared with other optimisation algorithms using test instances.•MA-RCGA was applied for solving a large-scale single-objective optimisation problem.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Random-key genetic algorithms were introduced by Bean (ORSA J. Comput. 6:154–160,
1994
) for solving sequencing problems in combinatorial optimization. Since then, they have been extended to handle a ...wide class of combinatorial optimization problems. This paper presents a tutorial on the implementation and use of biased random-key genetic algorithms for solving combinatorial optimization problems. Biased random-key genetic algorithms are a variant of random-key genetic algorithms, where one of the parents used for mating is biased to be of higher fitness than the other parent. After introducing the basics of biased random-key genetic algorithms, the paper discusses in some detail implementation issues, illustrating the ease in which sequential and parallel heuristics based on biased random-key genetic algorithms can be developed. A survey of applications that have recently appeared in the literature is also given.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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