Hybrid Gravitational Search Algorithm Research Articles

An optimal multipath routing protocol using hybrid gravitational search particle swarm optimization for secure communication

SummaryCommunicating large amounts of data requires an encryption solution that ensures fast and secure data transfer. Unfortunately, it faced privacy and security issues, resulting in higher power consumption, data scarcity, energy, diversity, data reliability, low security, and packet loss in the communication process. The goal of this research is to ensure data transfer while conserving energy and protecting data transmission in WSNs. Therefore, this paper proposes a multipath routing protocol using the HGSPSO algorithm, which is a combination of hybrid gravitational search algorithm (HGSA) and particle swarm optimization (PSO), to securely transmit data from one node to another node under public and private key cryptography. The PSO approach is utilized to resolve the energy hole problem by selecting the cluster head in the sink coverage area. The RSA can be employed for data encryption, data decryption, key generation, and sharing. The metrics like acceleration constants and inertial weights are used for the HGSPSO algorithm. The result of this implementation demonstrated that the scheme had a low energy consumption of 36%, a maximum packet delivery ratio (PDR) of 96%, a higher throughput rate of 88%, and a low packet loss of 39%.

Application of dynamic event-triggered hybrid gravitational search algorithm in pipeline leakage detection

In this paper, a hybrid gravitational search algorithm (GSA) based on the dynamic event-triggered mechanism, which is called DHGSA, is proposed to alleviate the slow exploitation problem of the GSA that may result in premature convergence and falling into local optimization. First, the DHGSA is divided into the exploration stage and the exploitation stage through the introduction of the population diversity. Then, the memory and social information exchange abilities of the particle swarm optimization algorithm are added in the DHGSA’s exploration stage, which can accelerate the convergence speed of the GSA. Next, the dynamic event-triggered mechanism (DETM) is added in the exploitation stage of the DHGSA, and a “celestial banishment strategy” is proposed, which improves the exploitation ability of the GSA and makes it have the ability to avoid falling into local optimal value. The derived results indicate that, compared with several other well-known search algorithms, the DHGSA has better performance on convergence speed, optimization accuracy and stability. Finally, the DHGSA is employed to optimize the parameters of the variational mode decomposition (VMD) algorithm for the oil and gas pipeline signal decomposition. The signal-to-noise ratio (SNR) of the three types of pipeline signals after denoising reaches 8.8406 dB, 9.0869 dB and 5.6880 dB, respectively. It is proved that the leakage signals can be denoised effectively in the research of the oil and gas pipeline signal processing.

Study of Full Controlled Green Time Roundabouts – An Intelligent Approach

When roundabouts face congestion problems, the transition to signalised roundabouts is considered a solution to the problem. The majority of studies have concentrated on how to calculate the optimal cycle length and signal timing to minimise congestion at roundabouts. To date, intelligence algorithms with multi-objectives such as queue length, number of stops, delay time, capacity and so on are widely used for calculating signal timing. Although roundabout congestion can be generated by the weaving zone reducing roundabout capacity, there have been minimal studies which take into account the density in the weaving zone. This study proposed a hybrid gravitational search algorithm – ABFO random forest regression with the following objectives: density, delay time and capacity to find the optimal cycle length and green time in each phase of Changwon city hall roundabout in South Korea as a case study. The optimal cycle length and green time were calculated in MATLAB and microscopic simulation VISSIM sought the effectiveness of a signalised roundabout. The result of the analysis demonstrated that signalised roundabouts with 102 seconds cycle length (phase 1 – 65 seconds of green time and phase 2 – 37 seconds of green time) can reduce density by 46.1%, delays by 32.8% and increase roundabout capacity by 14.8%.

Multi-objective optimal dispatching of combined cooling, heating and power using hybrid gravitational search algorithm and random forest regression: Towards the microgrid orientation

In order to eventually reach the net-zero carbon target, the low carbon transition demands substantial growth in the penetration of renewable generation in energy systems. It is essential to have enough flexible resources to prevent the curtailment of energy systems with high intermittent renewable output to run consistently. Therefore, this study focuses on the multi-objective optimal dispatching model of combined cooling, heating, and power (CCHP) microgrid with power-to-gas (P2G). The structure of the CCHP microgrid and the working principle of P2G are studied and analyzed. First, briefly described the multi-objective optimal dispatch model of the CCHP microgrid with P2G established with the goal of operational and environmental cost. Second, a hybrid gravitational search algorithm and random forest regression (GSA-RFR) are introduced to find out the optimal values. The proposed model verifies that P2G improves the wind abandonment capacity of the CCHP microgrid operation system. Furthermore, it reduces environmental costs and transforms environmental values into economic benefits during system operation. Noting that, the GSA-RFR improves the system economy by 7.13% comparatively.

HYBRID PARTICLE SWARM OPTIMIZATION-GRAVITATIONAL SEARCH ALGORITHMS DEEP LEARNING NETWORKS TO SIMULTANEOUSLY PROJECT MULTIPLE CRUDE OIL PRICE

The conventional linear econometric and statistical models are not effective for forecasting the nonlinear and complex nature of crude oil prices. Computational intelligence techniques and hybrid modelling principles have been proposed to address this issue. Multiple forecasts can be combined using linear or nonlinear methods to create an aggregate forecast. Currently, there is no study on the optimization of deep learning with a hybrid of gravitational search algorithm (GSA) and particle swarm optimization (PSO) for forecasting crude oil price benchmarks, including WTI, Brent, and Dubai. Additionally, most studies have focused only on using West Texas Intermediate (WTI) crude oil spot prices as their benchmark. A Bidirectional Long Short-Term Memory with hybrid gravitational search algorithm (GSA) and particle swarm optimization (PSO) to forecast Crude Oil prices is proposed. The proposed model outperformed FNNPSOGSA, FNNGA, LSTM and BiLSTM models with root mean square errors (RMSE) of 0.0029, 0.0011, and 0.0029, respectively. The proposed model is suited for Crude Oil forecasting.

An Intelligent Approach for Accurate Prediction of Chronic Diseases

Around the globe, chronic diseases pose a serious hazard to healthcare communities. The majority of the deaths are due to chronic diseases, and it causes burdens across the world. Through analyzing healthcare data and extracting patterns healthcare administrators, victims, and healthcare communities will get an advantage if the diseases are early predicted. The majority of the existing works focused on increasing the accuracy of the techniques but didn’t concentrate on other performance measures. Thus, the proposed work improves the early detection of chronic disease and safeguards the lives of the patients by increasing the specificity and sensitivity of the classifiers along with the accuracy. The proposed work used a hybrid optimization algorithm called the Hybrid Gravitational Search algorithm and Particle Swarm Optimization algorithm (HGSAPSO) to upgrade the detection of chronic diseases. Existing classifier parameters with their optimized parameters are compared and evaluated. Classifiers such as Artificial Neural Network (ANN), Support Vector Machines (SVM), K-Nearest Neighbor (Knn), and Decision tree (DT) are used. Health care data are obtained from the UCI machine learning repository to evaluate the proposed work. The proposed work is assessed on 6 benchmark datasets and the performance metrics such as Accuracy, Specificity, Sensitivity, F-measure, Recall, and Precision are compared. The experimental results exhibit that the proposed work attains better accuracy on Artificial Neural Network-Hybrid Gravitational Search algorithm and Particle Swarm Optimization algorithm (ANN-HGSAPSO) classifier compared to other classifiers. ANN-HGSAPSO provides 93% accuracy for Chronic Kidney Disease (CKD), Cardio Vascular Disease (CVD) 96%, Diabetes 82%, Hepatitis 94%, Wisconsin Breast Cancer (WBC) 91%, and for Liver disease dataset 96%.

Study of an Optimized Micro-Grid’s Operation with Electrical Vehicle-Based Hybridized Sustainable Algorithm

Recently, the expansion of energy communities has been aided by the lowering cost of storage technologies and the appearance of mechanisms for exchanging energy that is driven by economics. An amalgamation of different renewable energy sources, including solar, wind, geothermal, tidal, etc., is necessary to offer sustainable energy for smart cities. Furthermore, considering the induction of large-scale electric vehicles connected to the regional micro-grid, and causes of increase in the randomness and uncertainty of the load in a certain area, a solution that meets the community demands for electricity, heating, cooling, and transportation while using renewable energy is needed. This paper aims to define the impact of large-scale electric vehicles on the operation and management of the microgrid using a hybridized algorithm. First, with the use of the natural attributes of electric vehicles such as flexible loads, a large-scale electric vehicle response dispatch model is constructed. Second, three factors of micro-grid operation, management, and environmental pollution control costs with load fluctuation variance are discussed. Third, a hybrid gravitational search algorithm and random forest regression (GSA-RFR) approach is proposed to confirm the method’s authenticity and reliability. The constructed large-scale electric vehicle response dispatch model significantly improves the load smoothness of the micro-grid after the large-scale electric vehicles are connected and reduces the impact of the entire grid. The proposed hybridized optimization method was solved within 296.7 s, the time taken for electric vehicle users to charge from and discharge to the regional micro-grid, which improves the economy of the micro-grid, and realizes the effective management of the regional load. The weight coefficients λ1 and λ2 were found at 0.589 and 0.421, respectively. This study provides key findings and suggestions that can be useful to scholars and decisionmakers.

Predicting the environmental economic dispatch problem for reducing waste nonrenewable materials via an innovative constraint multi-objective Chimp Optimization Algorithm

The usage of conventional fossil fuels has aided fast economic growth while also having negative consequences, such as increased global warming and the destruction of the ecosystem. This paper proposes a novel swarm-based metaheuristic method called Chimp Optimization Algorithm (ChOA) to tackle the environmental, economic dispatch issue and reducing the waste nonrenewable materials. In this regard, two objective functions named fuel cost function and emission cost function are proposed. Unique constrained handling also solves the challenge of multi-objective optimization. Standard IEEE 30 bus with six generators and a 10-unit system are used to demonstrate the usefulness of ChOA. The result of ChOA is compared with Individual Best Memory Penalty Factor Grey Wolf Optimizer (IBMPF-GWO), Improved Whale Trainer (IWT), Chaotic Biogeography-Based Optimizer (CBBO), Non-Linear Migration BBO (NLBBO), Hybrid Gravitational Search Algorithm Particle Swarm Optimization (GSAPSO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Differential Evolution-Crossover Quantum Particle Swarm Optimization (DE-CQPSO), Salp Swarm Algorithm (SSA), Dragonfly Algorithm (DA), and Fuzzy Grasshopper Optimization Algorithm (FGOA) to confirm its efficiency. For both single- and multi-objective optimization, ChOA's assessment index and convergence rate are superior to other benchmark algorithms, regardless of whether the goal is to reduce emissions or to reduce fuel costs. The efficacy and robustness of the ChOA in handling environmental economic dispatch problems have been shown by discovering a good compromise value.

Innovative Optimal Nonstandard Tripping Protection Scheme for Radial and Meshed Microgrid Systems

The coordination of optimal overcurrent relays (OCRs) for modern power networks is nowadays one of the critical concerns due to the increase in the use of renewable energy sources. Modern grids connected to inverter-based distributed generations (IDGs) and synchronous distributed generations (SDGs) have a direct impact on fault currents and locations and then on the protection system. In this paper, a new optimal OCR coordination scheme has been developed based on the nonstandard time–current characteristics (NSTCC) approach. The proposed scheme can effectively minimize the impact of distributed generations (DGs) on OCR coordination by using two optimization techniques: genetic algorithm (GA) and hybrid gravitational search algorithm–sequential quadratic programming (GSA–SQP) algorithm. In addition, the proposed optimal OCR coordination scheme has successfully employed a new constraint reduction method for eliminating the considerable number of constraints in the coordination and tripping time formula by using only one variable dynamic coefficient. The proposed protection scheme has been applied in IEEE 9-bus and IEC MG systems as benchmark radial networks as well as IEEE 30-bus systems as meshed structures. The results of the proposed optimal OCR coordination scheme have been compared to standard and nonstandard characteristics reported in the literature. The results showed a significant improvement in terms of the protection system sensitivity and reliability by minimizing the operating time (OT) of OCRs and demonstrating the effectiveness of the proposed method throughout minimum and maximum fault modes.

A novel hybrid gravitational and pattern search algorithm based MPPT controller with ANN and perturb and observe for photovoltaic system

A novel hybrid gravitational and pattern search algorithm based MPPT controller with ANN and perturb and observe for photovoltaic system

Training a Feedforward Neural Network Using Hybrid Gravitational Search Algorithm with Dynamic Multiswarm Particle Swarm Optimization.

One of the most well-known methods for solving real-world and complex optimization problems is the gravitational search algorithm (GSA). The gravitational search technique suffers from a sluggish convergence rate and weak local search capabilities while solving complicated optimization problems. A unique hybrid population-based strategy is designed to tackle the problem by combining dynamic multiswarm particle swarm optimization with gravitational search algorithm (GSADMSPSO). In this manuscript, GSADMSPSO is used as novel training techniques for Feedforward Neural Networks (FNNs) in order to test the algorithm's efficiency in decreasing the issues of local minima trapping and existing evolutionary learning methods' poor convergence rate. A novel method GSADMSPSO distributes the primary population of masses into smaller subswarms, according to the proposed algorithm, and also stabilizes them by offering a new neighborhood plan. At this time, each agent (particle) increases its position and velocity by using the suggested algorithm's global search capability. The fundamental concept is to combine GSA's ability with DMSPSO's to improve the performance of a given algorithm's exploration and exploitation. The suggested algorithm's performance on a range of well-known benchmark test functions, GSA, and its variations is compared. The results of the experiments suggest that the proposed method outperforms the other variants in terms of convergence speed and avoiding local minima; FNNs are being trained.

Echo State Network Optimization using Hybrid-Structure Based Gravitational Search Algorithm with Square Quadratic Programming for Time Series Prediction

The Echo-State Network (ESN) is a robust recurrent neural network and a generalized form of classical neural networks in time-series model designs. ESN inherits a simple approach for training and demonstrates the high computational capability to solve non-linear problems. However, input weights and the reservoir's internal weights are pre-defined when optimizing with only the output weight matrix. This paper proposes a Hybrid Gravitational Search Algorithm (HGSA) to compute ESN output weights. In Gravitational Search Algorithm (GSA), Square Quadratic Programming (SQP) is united as a local search strategy to raise the standard GSA algorithm's efficiency. Later, an HGSA-SQP and the validation data set to establish the relation configuration of the ESN output weights. Experimental results indicate that the proposed configuration of HGSA-SQP-ESN is more efficient than the other conventional models of ESN with the minimum generalization error.

Optimal Energy Consumption Optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid Gravitational Search and Particle Swarm Optimization Algorithm

Buildings are the main energy consumers across the world, especially in urban communities. Building smartization, or the smartification of housing, therefore, is a major step towards energy grid smartization too. By controlling the energy consumption of lighting, heating, and cooling systems, energy consumption can be optimized. All or some part of the energy consumed in future smart buildings must be supplied by renewable energy sources (RES), which mitigates environmental impacts and reduces peak demand for electrical energy. In this paper, a new optimization algorithm is applied to solve the optimal energy consumption problem by considering the electric vehicles and demand response in smart homes. In this way, large power stations that work with fossil fuels will no longer be developed. The current study modeled and evaluated the performance of a smart house in the presence of electric vehicles (EVs) with bidirectional power exchangeability with the power grid, an energy storage system (ESS), and solar panels. Additionally, the solar RES and ESS for predicting solar-generated power prediction uncertainty have been considered in this work. Different case studies, including the sales of electrical energy resulting from PV panels’ generated power to the power grid, time-variable loads such as washing machines, and different demand response (DR) strategies based on energy price variations were taken into account to assess the economic and technical effects of EVs, BESS, and solar panels. The proposed model was simulated in MATLAB. A hybrid particle swarm optimization (PSO) and gravitational search (GS) algorithm were utilized for optimization. Scenario generation and reduction were performed via LHS and backward methods, respectively. Obtained results demonstrate that the proposed model minimizes the energy supply cost by considering the stochastic time of use (STOU) loads, EV, ESS, and PV system. Based on the results, the proposed model markedly reduced the electricity costs of the smart house.

Energy management and optimized operation of renewable sources and electric vehicles based on microgrid using hybrid gravitational search and pattern search algorithm

• Considering renewable energy resources. • Reporting superior solutions in the case of cost and execution time • Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management • Presenting Hybrid Gravitational Search and Pattern Search Algorithm The use of plug-in hybrid electric vehicles (PHEVs) can solve many environmental problems and energy crises around the world. Using a large number of PHEVs with high storage and control capabilities can enhance the flexibility of distribution networks. However, optimal management PHEVs with the presence of renewable energy sources (RESs) is one of the main challenges that must be addressed. The optimal management of various RESs along with PHEVs is possible in the form of a microgrid (MG). Moreover, the uncertainties of input parameters are successfully considered in the model development by Monte Carlo simulation (MCS). In the modelling, the uncertainties in PHEVs, load, RESs and energy price are considered and simulated for 24 h. The NiMH-Battery is also used to investigate the role of the storage device. The objective function is minimizing the total cost of the grid-connected MG including the costs of load supply, PHEVs charging demand and power losses. The optimization problem of this paper is solved using the hybrid gravitational search and pattern search (GSA-PS) algorithms. Simulation confirm the efficiency of the GSA-PS technique compared with conventional schemes. The results also show that the generation costs of the GSA-PS are considerably reduced than the classical optimization algorithms.

Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence.

In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniques. The measurement data were obtained from ultrasound, transient earth voltage, temperature and sound sensors. The AI classifiers used include artificial neural network (ANN) and support vector machine (SVM). The performance of both classifiers was optimized by an optimization technique, GSA. The advantages of GSA classification on AI in classifying the abnormal location in switchgears are easy implementation, fast convergence and low computational cost. For performance comparison, several well-known metaheuristic techniques were also applied on the AI classifiers. From the comparison between ANN and SVM without optimization by GSA, SVM yields 2% higher accuracy than ANN. However, ANN yields slightly higher accuracy than SVM after combining with GSA, which is in the range of 97%-99% compared to 95%-97% for SVM. On the other hand, GSA-SVM converges faster than GSA-ANN. Overall, it was found that combination of both AI classifiers with GSA yields better results than several well-known metaheuristic techniques.

Multi-strategy gravitational search algorithm for constrained global optimization in coordinative operation of multiple hydropower reservoirs and solar photovoltaic power plants

Recently, the solar photovoltaic power, a promising renewable energy, is witnessing a rapid development period. However, it is often difficult to perfectly capture the generation of solar photovoltaic plants because of various factors (like weather condition, solar radiation and human activities), increasing the operational risk and cost of power system. Hybrid energy system proves to be an effective measure to address this problem. Motivated by this practical necessity, this paper develops a novel hybrid gravitational search algorithm to solve the coordinative operation model of multiple hydropower reservoirs and solar photovoltaic power plants. In the proposed method, the gravitational search algorithm is set as the unified framework; the neighborhood search strategy is used to improve the convergence rate by considering the social information and individual experience; the adaptive mutation strategy is used to improve the population diversity by elite conservation and mutation operator; the modified elastic-ball strategy and constraint handling technique are used to enhance the solution feasibility. The simulation results of numerical functions demonstrate the superiority of the developed method in convergence rate and global search ability. The hydro–solar operation results in different cases show that compared with the traditional methods, the proposed method can yield high-quality scheduling schemes to alleviate the peak shaving pressure of power system. Thus, the novelty of this paper is to provide an effective HGSA method for solving the complex engineering optimization problem.

New CUK–SEPIC converter based photovoltaic power system with hybrid GSA–PSO algorithm employing MPPT for water pumping applications

This work proposes a CUK–SEPIC converter for photovoltaic water pumping application with maximum power point tracking (MPPT). For this a new hybrid gravitational search algorithm (GSA)–particle swarm optimisation (PSO) based MPPT method is proposed. The recent power electronics based CUK–SEPIC converter provides identical output with positive and negative ground reference. The novel converter minimises the ripple present in the supply current and provides optimum PV power extraction from solar module by integrating input and output magnetic cores of inductor. The proposed model is practically tested under changing solar insolation by employing switch reluctance motor for water pumping applications.

Hybrid gravitational search algorithm based model for optimizing coverage and connectivity in wireless sensor networks

Recently, the wireless sensor networks (WSNs) found its extensive application in surveillance and target tracking. For these two WSN applications, connectivity and coverage play a major role most particularly for target tracking. A large number of available sensor nodes track targets, during which a massive redundant data gets generated, which may minimize the system performance. Most particularly during the sensor node failure, the major intention of coverage and connectivity optimization model is to select less number of sensor nodes with maximum direct sensor node connectivity. But existing algorithms fail to achieve minimal node selection, therefore to mitigate the barriers of the traditional coverage algorithms, this paper proposed the hybrid Gravitational Search algorithm with social ski-driver (GSA-SSD) based model. This hybrid approach in target based WSN optimizes the coverage and connectivity requirement. By adapting the dynamic behaviour of SSD algorithm, the performance of GSA gets improved. Finally, the relative performance of the proposed hybrid GSA-SSD based optimization model is validated and compared with other optimization algorithms. On the basis of uncovered area rate and a number of sensor nodes the performance is evaluated. The results are implemented in the MATLAB simulation tool. Further, the performance enhancement in terms of uncovered area rate, number of selected active sensors, energy consumption, connectivity and network lifetime is achieved with randomly deployed nodes.

Optimal Placement and Sizing of Inverter-Based Distributed Generation Units and Shunt Capacitors in Distorted Distribution Systems Using a Hybrid Phasor Particle Swarm Optimization and Gravitational Search Algorithm

In this paper, a novel hybrid population-based meta-heuristic algorithm, called the hybrid Phasor Particle Swarm Optimization and Gravitational Search Algorithm (PPSOGSA), is proposed to solve the problem of optimal placement and sizing of inverter-based distributed generation (DG) units and shunt capacitors in radial distribution systems with linear and non-linear loads. The objective of the problem is reduction of active power losses considering constraints of the fundamental frequency active and reactive power balance, RMS voltage, and total harmonic distortion of voltage (THDV) at each bus of the network, as well as the branch flow constraints. The performance of the PPSOGSA-based approach is evaluated on the standard IEEE 33- and 69-bus test systems under sinusoidal and non-sinusoidal operating conditions. Compared to the original PPSO and GSA and other algorithms commonly used in the optimal sitting and sizing problem of DG units and shunt capacitors, it is found that the proposed algorithm has yielded better results.

Modeling the Maximum Magnetic Entropy Change of Doped Manganite Using a Grid Search-Based Extreme Learning Machine and Hybrid Gravitational Search-Based Support Vector Regression

The thermal response of a magnetic solid to an applied magnetic field constitutes magnetocaloric effect. The maximum magnetic entropy change (MMEC) is one of the quantitative parameters characterizing this effect, while the magnetic solids exhibiting magnetocaloric effect have great potential in magnetic refrigeration technology as they offer a green solution to the known pollutant-based refrigerants. In order to determine the MMEC of doped manganite and the influence of dopants on the magnetocaloric effect of doped manganite compounds, this work developed a grid search (GS)-based extreme learning machine (ELM) and hybrid gravitational search algorithm (GSA)-based support vector regression (SVR) for estimating the MMEC of doped manganite compounds using ionic radii and crystal lattice parameters as descriptors. Based on the root-mean-square error (RMSE), the developed GSA-SVR-radii model performs better than the existing genetic algorithm (GA)-SVR-ionic model in the literature by 27.09%, while the developed GSA-SVR-crystal model performs better than the existing GA-SVR-lattice model in the literature by 38.34%. Similarly, the developed ELM-GS-crystal model performs better than the existing GA-SVR-ionic model with a performance enhancement of 14.39% and 20.65% using the mean absolute error (MAE) and RMSE, respectively, as performance measuring parameters. The developed models also perform better than the existing models using correlation coefficient as the performance measuring parameter when validated with experimentally measured MMEC. The superior performance of the present models coupled with easy accessibility of the descriptors definitely will facilitate the synthesis of doped manganite compounds with a high magnetocaloric effect without experimental stress.