Gravity Search Algorithm Research Articles

Estimating Ross 308 Broiler Chicken Weight Through Integration of Random Forest Model and Metaheuristic Algorithms.

For accurate estimation of broiler chicken weight (CW), a novel hybrid method was developed in this study where several benchmark methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Differential Evolution (DE), and Gravity Search Algorithm (GSA), were employed to adjust the Random Forest (RF) hyperparameters. The performance of the RF models was compared with that of classic linear regression (LR). With this aim, data (temperature, relative humidity, feed consumption, and CW) were collected from six poultry farms in Samsun, Türkiye, covering both the summer and winter seasons between 2014 and 2021. The results demonstrated that PSO and ACO significantly enhanced the performance of the standard RF model in all periods. Specifically, the RF-PSO model achieved a significant improvement by reducing the Mean Absolute Error (MAE) by 5.081% to 60.707%, highlighting its superior prediction accuracy and efficiency. The RF-ACO model also showed remarkable MAE reductions, ranging from 3.066% to 43.399%, depending on the input combinations used. In addition, the computational time required to train the RF models with PSO and ACO was considerably low, indicating their computational efficiency. These improvements emphasize the effectiveness of the PSO and ACO algorithms in achieving more accurate predictions of CW.

Design and evaluation of self-tuning optimal control for vibration suppression of magnetorheological semi-active suspension

The objective of this paper is to enhance the vibration damping capabilities of magnetorheological (MR) semi-active suspension by utilizing a self-tuning LQG control method. Firstly, to determine the mechanical model of MR damper using the hyperbolic tangent model, mechanical experiments were performed with a damping force test machine under various input excitations. Experimental and simulation data were compared to confirm the accuracy of the mechanical model for MR damper. The MR damper is then incorporated into the vehicle’s semi-active suspension system to create a quarter-car suspension model. In order to tackle the problems associated with low control accuracy and poor dynamic adjustment in traditional LQG controllers where the weighting matrix coefficients are difficult to determine, a self-tuning LQG controller based on gravity search algorithm (GSA-LQG) was designed. Finally, the designed controller performance was evaluated by the simulation and experimental results obtained from the random and sinusoidal excitation roads. Experimental data reveal that when subjected to GSA-LQG control, the suspension control performance is considerably enhanced compared to the passive control, PID control and conventional LQG control suspension. Based on these outcome, it can be concluded that the proposed algorithm is effective and the experimental platform is feasible.

A new neuro-fuzzy controller based maximum power point tracking for a partially shaded grid-connected photovoltaic system

Today, renewable energy systems like photovoltaic system are widely used in various applications. Among the different types of microgrids, hybrid microgrids are the most used type, therefore, inverters should be used to exchange power between DC and AC sides. According to the existing economic issues, extracting the maximum possible power from these systems are an important issue. This paper presents a new neuro-fuzzy controller for achieving maximum power point tracking (MPPT) in a grid-connected PV system under partially shaded conditions. This controller uses the Gravity Search Algorithm (GSA) to track the global maximum power point (GMPP) of the presented grid-connected PV system. The method controls the grid-connected inverter at the desired voltage to achieve maximum power after receiving its required specifications from the system. The Matlab/Simulink software is used to evaluate the performance of the proposed method. The results show that the proposed method can track the maximum power point under uniform and partial shading conditions with high speed and accuracy. Specifically, the proposed algorithm improves the tracking speed and increases the power output compared to traditional methods. The neuro-fuzzy controller's adaptive capabilities allow it to respond efficiently to dynamic changes in shading, ensuring stable and optimal power output. These advantages make the proposed method a significant improvement over existing MPPT techniques.

Temperature compensation of a hybrid algorithm optimized neural network: Application to a 64-channel electronic pressure scanner

As a high-precision instrument, electronic pressure scanners have a crucial role in multi-point pressure measurements. However, the internal piezoresistive components drift with temperature, and this phenomenon has become a key factor restricting the accuracy. In this study, a hybrid algorithm based on back propagation neural network (BPNN) with particle swarm optimization (PSO) and gravity search algorithm (GSA) is reported for multi-channel temperature compensation to solve this problem Fundamentally, the global optimization search capability of GSA and the fast local convergence advantage of PSO are utilized so that the model parameters of the BPNN are guaranteed to be reliable and the efficiency of its execution is further improved. To this end, the temperature data between −40 °C and 70 °C obtained through the calibration experimental system are analyzed for compensation of the electronic pressure scanner from the 0 to 700 kPa adiabatic range. The results show that, compared with BP neural network, radial basis function (RBF) neural network, and PSO-BP method, the new PSOGSA-BP approach has higher accuracy in pressure sensor temperature compensation, in which the absolute error is only 0.2301 kPa and the full-scale error is 0.03% full scale. In addition, the method can be applied to electronic pressure scanners with stronger generalization and demonstrated to be suitable for temperature compensation.

Study on the hydraulic turbine modeling method based on IGSA-NN revision strategy

Abstract With the further increase of renewable energy such as wind power and photovoltaic in the power grid, the hydraulic turbine regulating system (HTRS) faces enormous challenges. As simulation modeling is the basis for studying the optimal control of HTRS, it is crucial to establish a high-precision model of HTRS. As the core component of the HTRS, the hydro-turbine contains extremely complex nonlinearity, which model accuracy directly affects the accuracy of the HTRS simulation platform. However, current modeling methods have certain disadvantages such as large modeling errors and making the structure of the HTRS simulation platform more complex. Therefore, this paper proposes a data modification strategy (IGSA-NN) based on the BP neural network (BPNN) and an improved gravity search algorithm (IGSA) and then achieves high-precision modeling of the hydro-turbine combining the BPNN and actual operation data. Finally, the accuracy of the hydro-turbine model is verified by the actual operation data, and the method to build the simulation platform of the HTRS which contains both real and virtual components is given. The research results have a certain guiding significance for hydro-turbine modeling.

Optimization technology of hydroelectric power plant unit speed control based on the constriction coefficient-based particle swarm gravitational search algorithm fusion model

In recent years, the integration of wind power into hydropower networks has become a trend. But after wind power is connected to the grid, its unstable power will have an impact on the stability of the power grid. In order to improve the impact of wind power grid connection on the stability of the power supply network, a water motor speed control fusion model considering wind power interference is proposed. Firstly, analyze the impact of wind power grid connection and construct a speed regulation model for grid connected units. Secondly, the gravity search algorithm is introduced to optimize the unit speed control parameters. Considering that the algorithm is prone to local convergence, the convergence factor particle swarm optimization algorithm is adopted to optimize the gravity search algorithm. In addition, considering the impact of wind power interference on unit regulation, the Hopf bifurcation theory is introduced to analyze the dynamic regulation parameter range of hydropower units, in order to meet the requirements of stable operation of the power grid. The research contribution is mainly reflected in suppressing wind power grid connection interference through the speed regulation of hydroelectric units. At the same time, considering the impact of multiple interferences on grid connection, an intelligent model is introduced to optimize the speed control parameters of the unit, thereby improving the stable impact of wind power grid connection on the water conservancy grid. In the simulation analysis of hydroelectric unit speed regulation, after adding 5 % disturbance signal, the proposed model under rigid water hammer disturbance could stabilize the unit's stable control within 15 s, which is superior to other models. In the simulation analysis of speed regulation under wind power disturbance, the proposed model could achieve the fastest and most stable speed regulation under rigid water hammer step wind power disturbance, with a duration of 60 s. The proposed model could respond quickly to small overshoot and overshoot in elastic water hammer slope type wind power disturbances. Therefore, the proposed speed regulation technology for hydropower units has excellent application effects in practical scenarios, which will provide effective technical references for wind power grid connection and hydropower unit regulation.

Research on Multi-Objective Flexible Job Shop Scheduling Problem with Setup and Handling Based on an Improved Shuffled Frog Leaping Algorithm

Flexible job shop scheduling problem (FJSP), widely prevalent in many intelligent manufacturing industries, is one of the most classic problems of production scheduling and combinatorial optimization. In actual manufacturing enterprises, the setup of machines and the handling of jobs have an important impact on the scheduling plan. Furthermore, there is a trend for a cluster of machines with similar functionalities to form a work center. Considering the above constraints, a new order-driven multi-equipment work center FJSP model with setup and handling including multiple objectives encompassing the minimization of the makespan, the number of machine shutdowns, and the number of handling batches is established. An improved shuffled frog leading algorithm is designed to solve it through the optimization of the initial solution population, the improvement of evolutionary operations, and the incorporation of Pareto sorting. The algorithm also combines the speed calculation method in the gravity search algorithm to enhance the stability of the solution search. Some standard FJSP data benchmarks have been selected to evaluate the effectiveness of the algorithm, and the experimental results confirm the satisfactory performance of the proposed algorithm. Finally, a problem example is designed to demonstrate the algorithm’s capability to generate an excellent scheduling plan.

Application Research of Intelligent Search System Based on Clustering Algorithm

In this paper, a multi-agent oriented personalized information retrieval method is proposed. The system is composed of 5 agents and 2 other components. The design highlights the personalization and real-time of the information obtained by users, and has the characteristics of intelligent retrieval and active retrieval. Then this paper studies it from three aspects: user model, system learning model and individual information model. Then a grouping gravity search algorithm is proposed. Compared with conventional gravity search, group gravity search is carried out for a specific decoding strategy. This algorithm is a new gravity search algorithm suitable for packet coding, which makes the packet search process approximate to the iterative optimization of classical gravity algorithm. Finally, the effectiveness of the proposed method is verified on various typical experimental cases. Experiments show that the proposed algorithm has higher classification performance than other intelligent group algorithms.

Predicting discharge coefficient of weir–orifice in closed conduit using a neuro-fuzzy model improved by multi-phase PSOGSA

This study investigates the viability of a strong algorithm (PSOGSA) merging particle swarm optimization (PSO) and gravity search algorithm (GSA) in tuning adaptive neuro-fuzzy system (ANFIS) parameters for modeling dimensionless experimental discharge of combined weir–orifices. The results are compared with the standard ANFIS and two hybrid models ANFIS tuned with PSO and GSA. The models are assessed by applying several dimensionless input parameters, consisting h/D (the ratio of upstream water depth to channel diameter), W/D (the ratio of orifice opening height to channel diameter), H/D (the ratio of plate height to channel diameter) and using comparison indices such as root-mean-square error and mean absolute error. The outcomes reveal that the new ANFIS-PSOGSA method provides superior accuracy in modeling dimensionless experimental discharge over the ANFIS-PSO, ANFIS-GSA and standard ANFIS method. Among the input parameters, the h/D was found to be the most effective input on modeling dimensionless experimental discharge while involving the H/D parameter deteriorated the models’ performances. The relative root-mean-square error differences between ANFIS-PSOGSA and ANFIS are found as 50% and 68.29% for pipe A and B, respectively. By implementing the ANFIS-PSOGSA, the accuracy of ANFIS-PSO and ANFIS-GSA is also improved in modeling dimensionless experimental discharge by 45.71% and 29.63% in pipe A and by 63.89% and 45.83% in pipe B with respect to root-mean-square error.

A Data‐Driven Method for Supporting Self‐Adapt Large‐Scale Group Decision‐Making: A Case Study on Resilient Design of Firm’s Product

Large‐scale group decision‐making (LSGDM) has emerged as a prominent research area in various domains, such as high technology and complex engineering problems. The advent of machine learning techniques has revolutionized LSGDM by introducing new data‐driven approaches. First, recurrent neural networks (RNNs) have been proposed as a data‐driven method to effectively learn and predict experts’ preferences. Second, a self‐adaptive method has been devised to optimize clustering parameters, considering their influence. The consensus‐reaching process facilitates the reverse optimization of these parameters. Third, a novel approach called analysis target cascading (ATC) has been suggested to address the limitations of traditional weighing methods used in previous LSGDM studies. ATC comprehensively investigates the potential game among multiple subgroups, thereby resolving the consensus‐reaching problem (CRP). Lastly, an improved artificial bee colony algorithm has been proposed to tackle the optimization problem presented in this study. This enhanced algorithm incorporates the levying mechanism and searching method from the gravity search algorithm. To validate the efficacy of the proposed methods, a case study involving a large‐scale interdisciplinary team has been conducted.

Droop Control Optimization for Improved Power Sharing in AC Islanded Microgrids Based on Centripetal Force Gravity Search Algorithm

The urgent demand for clean and renewable energy sources has led to the emergence of the microgrid (MG) concept. MGs are small grids connecting various micro-sources, such as diesel, photovoltaic wind, and fuel cells. They operate flexibly, connected to the grid, standalone, and in clusters. In AC MG control, a hierarchical system consists of three levels: primary, secondary, and tertiary. It monitors and ensures MG stability, power quality, and power sharing based on the specifications of governing protocols. Various challenging transient disturbances exist, such as generator tripping, secondary control failure due to communication delay, and drastic load changes. Although several optimal power sharing methods have been invented, they pose complex control requirements and provide limited improvement. Therefore, in this paper, a novel optimized droop control is proposed using a metaheuristic multi-objective evolutionary algorithm called the Centripetal Force-Gravity Search Algorithm (CF-GSA) to improve the droop performance of power sharing, voltage and frequency stability, and power quality. CF-GSA is an improved algorithm designed to address the issue of local solutions commonly encountered in optimization algorithms. The effectiveness and superiority of the proposed method are validated through a series of simulations. The results of these simulations show that the proposed multi-objective optimization droop control method works well to fix problems caused by power sharing errors in isolated AC microgrids that have to deal with high inductive loads and changes in line impedance.

Intelligent decision support system using nested ensemble approach for customer churn in the hotel industry

ABSTRACT Since customer retention costs much less than attracting new customer, the problem of customer churn is a major challenge in various fields of work and particularly Hotel Industry. In this research, a solution based on an intelligent decision support system using text mining and nested ensemble techniques is presented, which combines the advantages of stacking and voting methods. In the proposed system, after the text mining of the data collected from the hotels of Kish Island, the effective feature selection is done using the gravity search algorithm. In the first level of nested ensemble technique method, stacking deep learning methods are used. Voting is used in the MetaClassifier section, which includes Random Forest, Xgboost and Naïve Bayes methods. The results of the implementation and comparison of the proposed system, show that the performance of the proposed system has increased the accuracy by 0.04 compared to the best existing method.

Attitude control of a quadrotor using PID controller based on differential evolution algorithm

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency.

A New Efficient Hybrid Approach for Machine Learning-Based Firefly Optimization

Optimization is the task of minimizing or maximizing an objective function f(x) parameterized by x. A series of effective numerical optimization methods have become popular for improving the performance and efficiency of other methods characterized by high-quality solutions and high convergence speed. In recent years, there are a lot of interest in hybrid metaheuristics, where more than one method is ideally combined into one new method that has the ability to solve many problems rapidly and efficiently. The basic concept of the proposed method is based on the addition of the acceleration part of the Gravity Search Algorithm (GSA) model in the Firefly Algorithm (FA) model and creating new individuals. Some standard objective functions are used to compare the hybrid (FAGSA) method with FA and the traditional GSA to find the optimal solution. Simulation results obtained by MATLAB R2015a indicate that the hybrid algorithm has the ability to cross the local optimum limits with a faster convergence than the luminous Fireflies algorithm and the ordinary gravity search algorithm. Therefore, this paper proposes a new numerical optimization method based on integrating the properties of the two methods (luminous fireflies and gravity research). In most cases, the proposed method usually gives better results than the original methods individually.

Parallel Batch Processing Machine Scheduling Under Two-Dimensional Bin-Packing Constraints

The parallel batch processing machine scheduling problem with two-dimensional bin packing constraints (PBS-2D) has appeared in many industrial environments, but the research on this problem is still not sufficient. In this article, we use the 2-D rectangular packing constraints to describe the spatial geometric layout of jobs more accurately, and propose a mixed integer linear programming (MILP) model to minimize the makespan of PBS-2D. According to the in-depth analysis of the problem properties, PBS-2D is separated into three subproblems, which are job-machine assignment, 2-D job placement, and job sequence optimization. An improved biased random key genetic algorithm (OBRKGA) is proposed to solve job-machine assignment and job sequence optimization. This algorithm generates the initial population based on the idea of orthogonal experimental design, so that the whole solution space can be scanned evenly when generating the initial solution, making the algorithm be more likely to find better solutions for subsequent evolution. At the same time, a bin packing algorithm based on best fit is designed to handle 2-D job placement problem. Finally, the algorithms are tested on a large number of randomly generated instances, the results show that OBRKGA outperformed the original BRKGA, the classical moth-flame optimization algorithm, particle swarm optimization algorithm, gravity search algorithm, and MILP.

Study on Adaptive Sliding Mode Fault-tolerant Control of 1/4 Vehicle Electromagnetic Suspension

An adaptive sliding mode fault-tolerant manage approach is proposed to remedy the trouble of actuator acquire failure of the electromagnetic hybrid energetic suspension device and the hassle of automobile manipulate balance degradation induced by using the nonlinear pressure of spring and damper. The failure thing is chosen to symbolize the actuator fault size, and the actuator fault parameter mannequin is established. Combining sliding mode manipulate with adaptive control, an adaptive sliding mode fault-tolerant controller for lively suspension primarily based on an correct mannequin is designed. Based on the Lyapunov balance theory, the steadiness of the machine is proved; and the Gravity search algorithm (GSA) and linear matrix inequality (LMIS) are used to optimize the sliding floor parameters, and the most beneficial answer is chosen in accordance to the health function. The simulation effects exhibit that in contrast with passive suspension, the root capability rectangular values of sprung mass acceleration, suspension dynamic deflection, and tire dynamic load underneath adaptive sliding mode fault-tolerant manipulate approach decrease by way of 41.7%, 38.4%, and 36.5% respectively. It suggests that the designed adaptive sliding mode fault-tolerant controller is right and can enhance car experience comfort.

Frequency Control of Power System with Distributed Sources by Adaptive Type 2 Fuzzy PID Controller

The unpredictability of solar and wind energy sources affects contemporary power networks and adds frequency variations. An appropriate intelligent controller is necessary to balance electricity between generation and demand. As a result, in this research, we present a sine-cosine adaptive improved equilibrium optimization (SCaIEO) method tuned to Adaptive Type 2 Fuzzy PID Controller (AT2FPID) for frequency management of cutting-edge power systems. The efficiency of the SCaIEO approach is assessed by comparing it to the original equilibrium optimization (EO) and other comparable algorithms for the test function. Moreover, engineering applications of the SCaIEO technique are carried out by constructing an AT2FPID controller to manage the frequency of power systems that include renewable energy and dispersed sources. First, we show that SCaIEO outperforms EO, Particle Swarm Optimization, Genetic Algorithm, Moth Flame Optimization, and Gravitational Search Algorithm in the PID controller (Gravity Search Algorithm). The AT2FPID is next evaluated, and the SCaIEO AT2FPID controller’s dominance is proven by equating the outcome to the original, PID Type 1 Fuzzy PID, Type 2 Fuzzy PID controller.

Predicting Discharge Coefficient of Triangular Side Orifice Using LSSVM Optimized by Gravity Search Algorithm

Side orifices are commonly installed in the side of a main channel to spill or divert some of the flow from the source channel to lateral channels. The aim of the present study is the accurate estimation of the discharge coefficient for flow through triangular (Δ-shaped) side orifices by applying three data-driven models including support vector machine (SVM), least squares support vector machine (LSSVM) and least squares support vector machine improved by gravity search algorithm (LSSVM-GSA). The discharge coefficient was estimated by utilizing five dimensionless variables resulted from experimental data (570 runs). Five different scenarios were applied based on the input variables. The models were evaluated through several statistical indices and graphical charts. The results showed that all of the models could successfully estimate the discharge coefficient of Δ-shaped side orifices with adequate accuracy. However, the LSSVM-GSA produced the best performance for the input combination of all variables with the highest coefficients of determination (R2) and Nash–Sutcliffe efficiency (NSE), equal to 0.965 and 0.993, and the least root mean square error (RMSE) and mean absolute error (MAE), equal to 0.0099 and 0.0077, respectively. The LSSVM-GSA improved the RMSE of the SVM and LSSVM by 26% and 20% in estimating the discharge coefficient. Furthermore, the ratio of orifice crest height to orifice height (W/H) was identified as having the highest influence on the discharge coefficient of triangular side orifices among the various input variables.

A wind speed forecasting model based on multi-objective algorithm and interpretability learning

More accurate and reliable wind speed forecasting results can provide an effective assessment of wind energy resources and improve the efficiency of wind energy utilization. Therefore, this paper is devoted to constructing accurate wind speed forecasting models and quantitatively analyzing the models by using interpretable analysis. Firstly, a multivariate wind speed forecasting model (PMI-CMOGSA-RELM) is proposed based on machine learning methods and a clustering-based multi-objective gravity search algorithm (CMOGSA). Among them, wavelet packet decomposition (WPD) is used for noise reduction, candidate input feature pool and partial mutual information (PMI) are used for feature selection, and CMOGSA is used to optimize the final combined weights. Secondly, a new evaluation metric, Absolute Error Coverage Probability (AECP), is proposed to better evaluate forecasting accuracy. Then, post-hoc attribution analysis methods and visualization tools are used to analyze the forecasting model interpretability to help us better analyze the robustness and reliability of the forecasted results. Finally, this paper validates and evaluates the proposed model with two data sets of different resolutions. The experimental results not only prove the rationality of the AECP evaluation criteria, but also demonstrate that the proposed model has smaller errors, higher estimation accuracy, and is easier to understand.

Navigating Uncertainty: A Comprehensive Approach to Risk Management in R&D Projects with the Gravity Search Algorithm Based MCDM

Navigating Uncertainty: A Comprehensive Approach to Risk Management in R&D Projects with the Gravity Search Algorithm Based MCDM