Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance

Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.

A Rule-Based Approach for Grey Hole Attack Prediction in Wireless Sensor Networks

The Wireless Sensor Networks(WSN)are vulnerable to assaults due to the fact that the devices connected to them have a reliable connection to the inter-net.A malicious node acts as the controller and uses a grey hole attack to get the data from all of the other nodes in the network.Additionally,the nodes are dis-carding and modifying the data packets according to the requirements of the sys-tem.The assault modifies the fundamental concept of the WSNs,which is that different devices should communicate with one another.In the proposed system,there is a fuzzy idea offered for the purpose of preventing the grey hole attack from making effective communication among the WSN devices.The currently available model is unable to recognise the myriad of different kinds of attacks.The fuzzy engine identified suspicious actions by utilising the rules that were gen-erated to make a prediction about the malicious node that would halt the process.Experiments conducted using simulation are used to determine delay,accuracy,energy consumption,throughput,and the ratio of packets successfully delivered.It stands in contrast to the model that was suggested,as well as the methodologies that are currently being used,and analogue behavioural modelling.In comparison to the existing method,the proposed model achieves an accuracy rate of 45 per-cent,a packet delivery ratio of 79 percent,and a reduction in energy usage of around 35.6 percent.These results from the simulation demonstrate that the fuzzy grey detection technique that was presented has the potential to increase the net-work’s capability of detecting grey hole assaults.

A hybrid agent⁃based machine learning method for human⁃centred energy consumption prediction

Occupant behaviour has significant impacts on the performance of machine learning algorithms when predicting building energy consumption.Due to a variety of reasons(e.g.,underperforming building energy management systems or restrictions due to privacy policies),the availability of occupational data has long been an obstacle that hinders the performance of machine learning algorithms in predicting building energy consumption.Therefore,this study proposed an agent⁃based machine learning model whereby agent⁃based modelling was employed to generate simulated occupational data as input features for machine learning algorithms for building energy consumption prediction.Boruta feature selection was also introduced in this study to select all relevant features.The results indicated that the performances of machine learning algorithms in predicting building energy consumption were significantly improved when using simulated occupational data,with even greater improvements after conducting Boruta feature selection.

Prediction of Total Output Value of Construction Industry in Jiangxi Province Based on Grey Prediction Model

In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,and based on the existing data,the total output value of construction industry in Jiangxi Province in the next five years is predicted.The results show that the grey prediction model has a good prediction effect,and the error between the predicted value and the measured value is within 14%,which provides a basis for policy adjustment and resource optimization.

Prediction method of highway pavement rutting based on the grey theory

In order to make a scientific pavement maintenance decision, a grey-theory-based prediction methodological framework is proposed to predict pavement performance. Based on the field pavement rutting data,analysis of variance （ANOVA）was first used to study the influence of different factors on pavement rutting. Cluster analysis was then employed to investigate the rutting development trend.Based on the clustering results,the grey theory was applied to build pavement rutting models for each cluster, which can effectively reduce the complexity of the predictive model.The results show that axial load and asphalt binder type play important roles in rutting development.The prediction model is capable of capturing the uncertainty in the pavement performance prediction process and can meet the requirements of highway pavement maintenance,and,therefore,has a wide application prospects.

Study on the Yield Prediction Model of Processing Tomato Based on the Grey System Theory

[Objective] The research aimed to study the yield prediction model of processing tomato based on the grey system theory.[Method] The variation trend of processing tomato yield was studied by using the grey system theory,and GM（1,1）grey model of processing tomato yield prediction was established.The processing tomato yield in Xinjiang during 2001-2009 was as the example to carry out the instance analysis.[Result] The model had the high forecast accuracy and strong generalization ability,and was reliable for the prediction of recent processing tomato yield.[Conclusion] The research provided the reference for the macro-control of tomato industry,the processing and storage of tomato in Xinjiang.

Prediction model of interval grey number based on DGM(1,1)

In grey system theory,the studies in the field of grey prediction model are focused on real number sequences,rather than grey number ones.Hereby,a prediction model based on interval grey number sequences is proposed.By mining the geometric features of interval grey number sequences on a two-dimensional surface,all the interval grey numbers are converted into real numbers by means of certain algorithm,and then the prediction model is established based on those real number sequences.The entire process avoids the algebraic operations of grey number,and the prediction problem of interval grey number is usefully solved.Ultimately,through an example＇s program simulation,the validity and practicability of this novel model are verified.

Fault Prediction Based on Dynamic Model and Grey Time Series Model in Chemical Processes

This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.

Equivalency and unbiasedness of grey prediction models

In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results show that all the grey prediction models that are strictly derived from x^（0）（k） ＋az^（1）（k） = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homogeneous exponential sequence can be accomplished. However, the models derived from dx^（1）/dt ＋ ax^（1）= b are only close to those derived from x^（0）（k） ＋ az^（1）（k） = b provided that ｜a｜ has to satisfy｜a｜ 0.1; neither could the unbiased simulation for the homogeneous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.

Application of Grey System Theory to tree growth prediction

Based on Grey System theory, tree growth prediction models are developed by using 202 temporary plots and 206 stem analysis trees of Dahurian larch (Larix gemlinii Rupr) in 10 forestry bureaus of Yakeshi Forestry Administrative Bureau in Daxing’an Mountains of the Inner Mongolia Autonomous Region. By residual and posterior tests, their precisions are qualified. With several data, tree growth can be predicted using Grey System models. For DBH and volume, the fitting results of Grey System models are better than that of statistical models.

Grey Prediction of Siltation in North Channel of Yangtze Estuary

-The North Channel in the Yangtze Estuary is one of sea-leading waterways of Shanghai Harbour, in which yearly dredging volume reaches over ten million cubic meters, and it tends to increase year by year. Based on the channel regime similarity and through the relational grade, a GM (2, 1) is set up. It reveals the course of development of channel regime similarity under the action of various factors, and predicts the siltation volume in the nearest future which is the basis of dredging planning for relevant dredging departments.

Prediction of leaching rate in heap leaching process by grey dynamic model GDM(1,1)

The method of developing GM（1,1） model is extended on the basis of grey system theory. Conditions for the transfer function that improve smoothness of original data sequence and decrease the revert error are given. The grey dynamic model is first combined with the transfer function to predict the leaching rate in heap leaching process. The results show that high prediction accuracy can be expected by using the proposed method. This provides a new approach to realize prediction and control of the future behavior of leaching kinetics.

Grey Prediction Fuzzy Control of the Target Tracking System in a Robot Weapon

Grey modeling can be used to predict the behavioral development of a system and find out the lead control values of the system. By using fuzzy inference, PID parameters can be adjusted on line by the fuzzy controller with PID parameters self-tuning. According to the characteristics of target tracking system in a robot weapon, grey prediction theory and fuzzy PID control ideas are combined. A grey prediction mathematical model is constructed and a fuzzy PID controller with grey prediction was developed. Simulation result shows fuzzy PID control algorithm with grey prediction is an efficient method that can improve the control equality and robustness of traditional PID control and fuzzy PID control, and has much better performance for target tracking.

On the Flood Disasters in the Lower Jingjiang Reaches: Grey Prediction Model and Application

In the light of the historical substantial data (covering a 70-year period) collected in the Lower Jingjiang segment and aided by topological grey method, here we attempt to characterize the occurrence and future trend of flood calamities in the study area. Our findings indicate that overall the high-frequent flood disasters with middle to lower damage prevail at present. A series of dramatic flood waves will appear in the years of 2016, 2022, 2030 and 2042, particularly a destructive flood will occur between 2041 and 2045 in the Lower Jingjiang reaches. Typical of sensitive response to flood hazards in close association with its special geographical location, the lower Jingjiang segment hereby can reflect the development trend of floods in the middle Yangtze reaches. According to the results, a good fitness was revealed between the prediction and practical values. This actually hints that the topological grey method is an effective mathematical means of resolving problems containing uncertainty and indetermination, thus providing valuable information for the flood prediction in the middle Yangtze catchment.

The softening prediction of HSLA steel heat-affected zone based on the grey system

The high-strength low-alloy（ HSLA ） steel heat-affected zone （HAZ）softening was predicted using a grey model. HSLA steel DILLIMAX690E, NK-HITEN61OU2 and BHW35 were taken as examples in the research on ultra-narrow gap automatic welding technology. Test results turned out to be that the errors between the values calculated by the Grey Model （GM） （ 1,1 ） model and their actual value were less than 2%, indicating that the grey prediction method could accurately reflect the actual situation of the high-strength low-alloy steel heat-affected zone softening. This method will play a crucial role in guiding the applications of HSLA steel welded structures in the future.

Prediction of Wind Speed Using a Hybrid Regression-Optimization Approach

Predicting wind speed is a complex task that involves analyzing various meteorological factors such as temperature, humidity, atmospheric pressure, and topography. There are different approaches that can be used to predict wind speed, and a hybrid optimization approach is one of them. In this paper, the hybrid optimization approach combines a multiple linear regression approach with an optimization technique to achieve better results. In the context of wind speed prediction, this hybrid optimization approach can be used to improve the accuracy of existing prediction models. Here, a Grey Wolf Optimizer based Wind Speed Prediction (GWO-WSP) method is proposed. This approach is tested on the 2016, 2017, 2018, and 2019 Raw Data files from the Great Lakes Environmental Research Laboratories and the National Oceanic and Atmospheric Administration’s (GLERL-NOAA) Chicago Metadata Archive. The test results show that the implementation is successful and the approach yields accurate and feasible results. The computation time for execution of the algorithm is also superior compared to the existing methods in literature.

Grey GM(1,1) Model with Function-Transfer Method for Wear Trend Prediction and its Application

Trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the existing known information is used to infer the unknown information's character, state and development trend in a fault pattern, and to make possible forecasting and decisions for future development. It involves the whitenization of a Grey process. But the traditional equal time interval Grey GM (1,1) model requires equal interval data and needs to bring about accumulating addition generation and reversion calculations. Its calculation is very complex. However, the non equal interval Grey GM (1,1) model decreases the condition of the primitive data when establishing a model, but its requirement is still higher and the data were pre processed. The abrasion primitive data of plant could not always satisfy these modeling requirements. Therefore, it establishes a division method suited for general data modeling and estimating parameters of GM (1,1), the standard error coefficient that was applied to judge accuracy height of the model was put forward; further, the function transform to forecast plant abrasion trend and assess GM (1,1) parameter was established. These two models need not pre process the primitive data. It is not only suited for equal interval data modeling, but also for non equal interval data modeling. Its calculation is simple and convenient to use. The oil spectrum analysis acted as an example. The two GM (1,1) models put forward in this paper and the new information model and its comprehensive usage were investigated. The example shows that the two models are simple and practical, and worth expanding and applying in plant fault diagnosis.

Prediction of syphilis incident rate and number in China based on the GM(1,1)grey model

Objective:To explore the feasibility of using grey model GM(1,1)model to predict syphilis,and to provide a theoretical basis for the health sector to develop corresponding strategies.Methods:GM(1,1)model was used to construct and simulate the incident rate and case number of syphilis in China from 2009 to 2018 to predict the change trend.Results:The GM(1,1)prediction model of syphilis incident rate was x^(1)(k+1)=929.367901 e(0.029413k)-906.297901.The GM(1,1)prediction model for the number of syphilis patients was x^(1)(k+1)=1060.278025 e(0.034280k)-1029.639925.For syphilis incidence model,the posterior difference ratio was 0.19819 and the probability of small error was 1.For the syphilis incident number model,the posterior difference ratio was 0.18450 and the probability of small error was 1.The above models have good fitting accuracy with excellent grade level and can be predicted by extrapolation and predicted that the syphilis incidence in 2019-2021 may be 36.15 per 100,000,37.23 per 100,000 and 38.34 per 100,000,respectively.From 2019 to 2021,the number of incident syphilis cases in China may be 503,406,520,962 and 539,130,respectively.Conclusion:The GM(1,1)model can well fit and predict the change trend of syphilis incidence in time series.The prediction model showed that the incidence of syphilis may continue to increase and the number of syphilis cases per year may continue to increase substantially.More effort is needed to strengthen the prevention and treatment of venereal disease,reduce venereal harm to the population and improve the early detection rate of syphilis.

Grey forewarning and prediction for mine water inflowing catastrophe periods

Based on the theory of grey system, established GM （1, 1） grey catastrophe predict model for the first time in order to forecast the catastrophe periods of mine water inflowing （not the volume of water inflowing）. After establishing the grey predict system of the catastrophe regularity of 10 month-average volume of water inflowing, the grey forewarning for mine water inflowing catastrophe periods was established which was used to analyze water disaster in 400 meter level of Wennan Colliery. Based on residual analysis, it shows that the result of grey predict system is almost close to the actual value. And the scene actual result also shows the reliability of prediction. Both the theoretical analysis and the scene actual result indicate feasibility and reliability of the method of grey catastrophe predict system.

Method discussion for quick response grey prediction of stronger aftershocks of an earthquake sequence

In this paper, we take occurrence process of early strong aftershocks of a main after shock type′s earthquake sequence as a complex grey system, and introduce predicting method for its stronger aftershocks by grey predicting theory. Through inspection prediction for 1998 Zhangbei M S=6.2 earthquake sequence, it shows that the grey predicting method maybe has active significance for the investigation of quick response prediction problems of stronger aftershocks of an earthquake sequence.