Grey Wolf Optimizer Research Articles

Improvement of soft clay soil with a combination of pre-gelatinized corn starch and nanoparticle agro waste: A multi-objective grey wolf optimizer approach

The present study aimed to assess the potential of a bio-inspired algorithm, multi-objective grey wolf optimization algorithm (MOGWO), to optimize the strength properties (California bearing ratio (CBR) and unconfined compressive strength (UCS)) of an expansive subgrade soil. This optimization process involves the use of two additives, namely a bio-polymer, pregelatinized corn starch (PGCS), and a nanoparticle agro waste, rice husk ash (RHA), blended with the soil in different mix ratios determined by a 32 factorial experimental design. The CBR samples were cured for 7 days, while the UCS samples were cured for 1, 7, and 28 days. To optimize the expansive subgrade soil strength, regression models were developed using PGCS and RHA as predictors for CBR and UCS, serving as fitness functions in the slightly modified MOGWO optimization technique. Next, the optimization analysis produced non-dominated solutions. The results obtained from the laboratory experiments and optimization analysis revealed that there was significant improvement in the UCS and CBR of the soil. These improvements can be attributed to the pozzolanic reaction between the soil-RHA matrix, the formation of intercalated and exfoliated nanocomposites, and the hydrophilic interaction of PGCS. By applying the slightly modified MOGWO technique, the study achieved optimal enhancements in UCS (710.3 kN/m2) and CBR (24.2%) when the expansive subgrade soil was mixed with 0.2637% PGCS and 12.2413% RHA. The results demonstrate the potential of the MOGWO technique in improving the properties of expansive subgrade soil.

Computed Torque-NN-GWO Dynamic Hybrid Control of Manipulator Robotic Arm

Robotics involves designing, simulating, and controlling robots for various industries and daily tasks. Manipulator arm robots are precisionoriented industrial robots designed for one-plane movement. Planar robotic manipulators are used in cargo loading, assembly lines, handling radioactive materials, and military applications. In this study theoretical analysis of manipulator robot kinematics using Denavit-Hartenberg notation and the Euler-Lagrange method for a three-degree of freedom (3-DOF) system, requiring an advanced controller for accurate control. A new hybrid controller, Computed Torque Control-Neural-Network -Grey Wolf Optimization (CTC-NNGWO), is proposed for path planning of 3-DOF MR. It uses kinematics and dynamic models, computes torque magnitudes of stepper motors, and denormalizes output values from NN. MATLAB programming is used for the simulation of the theoretical result. A comparison study of CTC-NN-GWO and CTC-PSO found that CTC-NN-GWO outperforms CTC-PSO in minimizing path planning errors, the maximal value of the error of joint angular position 𝑒𝜃1 is (0.29 rad), whereas 𝑒𝜃2 and 𝑒𝜃3 are approximately (0.15 rad) and (0.07 rad), respectively, also, reducing torque magnitudes, improving the performance of 3-DOF MR. Index Terms— Manipulator robotic, Grey wolf, Neural network, Path planning, Computed torque.

Using ensemble machine learning and metaheuristic optimization for modelling the elastic modulus of geopolymer concrete

Geopolymer concrete emerges as a sustainable and durable alternative to conventional concrete, addressing its high carbon footprint and enhanced durability. The distinct properties of geopolymer concrete, governed by supplementary cementitious materials and alkaline activators, promise reduced environmental impact and improved structural resilience. However, its complex composition complicates the prediction of mechanical properties such as the elastic modulus, crucial for structural applications. This study introduces an innovative approach using the eXtreme Gradient Boosting (XGBoost) technique integrated with the multi-objective grey wolf optimizer to model the elastic modulus of geopolymer concrete. By dynamically selecting influential features and optimizing model accuracy, this methodology advances beyond traditional empirical models, which fail to capture the nonlinear interactions intrinsic to geopolymer concrete. Utilizing a comprehensive database gathered from extensive literature, 22 potential variables were examined that influence geopolymer concrete’s elastic modulus. After mitigating multicollinearity and optimizing hyperparameters via Bayesian optimization, six XGBoost models were developed with different combinations of input variables, revealing compressive strength and total water content as pivotal predictors. The findings illustrate the models’ precision, with the trade-off between prediction accuracy and model simplicity visualized through the relationship between the number of input variables and prediction error. The study culminates in a user-friendly graphical user interface that enables easy prediction of geopolymer concrete’s elastic modulus and fosters educational engagement. This interface, available online, underscores the practicality and accessibility of advanced machine learning predictions. Overall, this research not only provides a robust predictive framework for geopolymer concrete’s elastic modulus using optimized input variables but also enhances the understanding of its underlying determinants, contributing to the advancement of sustainable construction materials.

An intelligent data-driven investigation and optimization integrated with an eco-friendly thermal design approach for a marine diesel engine to study its waste-to-liquefied hydrogen generation potential

The waste heat generated by vehicles, especially heavy-duty engines, has motivated the current research to propose a novel thermal design for a ship's engine. Hence, the suggested system comprises an organic-flash power plant, a bi-evaporator cooling subsystem, a thermal desalination cycle, a water electrolysis-based hydrogen generation unit, and a Claude unit for liquefying hydrogen. The innovative aspects associated with the proposed design include utilizing a novel cascade heat recovery process aimed at reduced irreversibility for the engine's waste and the integration of bi-evaporator technology and Claude unit to facilitate the process of waste-to-liquefied hydrogen generation for a marine engine for improved handling. In addition, the research includes an intelligent study that comprehensively analyzes and optimizes the proposed system in terms of long-term sustainability and economic feasibility. For this purpose, a data-driven optimization technique is implemented using artificial neural networks in combination with multi-objective grey wolf optimization. The aims of the research encompass the optimization of the sustainability index and unit cost of liquefied hydrogen. The research findings indicate that the vapor generator’s terminal temperature difference exerts the most substantial influence on performance metrics, as evidenced by a mean sensitivity index of 0.368. Also, the mentioned objective functions exhibit values of 1.139 and 7.60 $/kg, respectively. Besides, the optimum exergy destruction rate is 124.3 kW, the total investment cost rate is 4.94 $/year, the specific cost of products is 74.87 /GJ, the payback period is 2.2 years, and the net present value is 7.48 M$.

Advancements in solar photovoltaic modelling: selective opposition-based variable weighted grey wolf optimizer with improved Newton–Raphson analysis

Advancements in solar photovoltaic modelling: selective opposition-based variable weighted grey wolf optimizer with improved Newton–Raphson analysis

Grey Wolf Optimization algorithm with random local optimal regulation and first-element dominance

Due to the classical Grey Wolf algorithm GWO does not consider the characteristics of the local information of individual in population, a novel local random optimization strategy is proposed to make up for the defect of GWO. In this method, several points in the neighborhood of the current location of each individual are selected at random in the axial direction as candidates, and the best points are selected to participate in the renewal decision of the individual. Furthermore, in our experiments, a special first-element dominance characteristic is found and can greatly improve the combination effect of global and local information. In order to ensure that all constraints are not violated in the process of constraint optimization in industrial design, the random mixed population initialization method is proposed to generate population individuals that meet the constraint requirements and contain boundary values randomly. In addition, a treatment method of shrinking in a specific direction is proposed for dealing with individuals who cross the boundary. Experimental results on several test function sets show that compared with recent improved algorithms for GWO, the proposed algorithm has obvious advantages in fitness value, convergence speed and stability.

A Multi-Strategy Collaborative Grey Wolf Optimization Algorithm for UAV Path Planning

The Grey Wolf Optimization Algorithm (GWO) is a member of the swarm intelligence algorithm family, which possesses the highlights of easy realization, simple parameter settings and wide applicability. However, in some large-scale application problems, the grey wolf optimization algorithm easily gets trapped in local optima, exhibits poor global exploration ability and suffers from premature convergence. Since grey wolf’s update is guided only by the best three wolves, it leads to low population multiplicity and poor global exploration capacity. In response to the above issues, we design a multi-strategy collaborative grey wolf optimization algorithm (NOGWO). Firstly, we use a random walk strategy to extend the exploration scope and enhance the algorithm’s global exploration capacity. Secondly, we add an opposition-based learning model influenced by refraction principle to generate an opposite solution for each population, thereby improving population multiplicity and preventing the algorithm from being attracted to local optima. Finally, to balance local exploration and global exploration and elevate the convergence effect, we introduce a novel convergent factor. We conduct experimental testing on NOGWO by using 30 CEC2017 test functions. The experimental outcomes indicate that compared with GWO and some swarm intelligence algorithms, NOGWO has better global exploration capacity and convergence accuracy. In addition, we also apply NOGWO to three engineering problems and an unmanned aerial vehicle path planning problem. The outcomes of the experiment suggest that NOGWO performs well in solving these practical problems.

Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand

The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately. Moreover, SVR was employed in conjunction with three robust optimization algorithms: the firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO), to construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 for elastic modulus (E), and 242 for split tensile strength (STS), the models were evaluated with statistical metrics and interpreted using the SHapley Additive exPlanation (SHAP) technique. The SVR-GWO hybrid model demonstrated exceptional accuracy in predicting waste foundry sand concrete (WFSC) strength characteristics. The SVR-GWO hybrid model exhibited correlation coefficient values (R) of 0.999 for CS and E, and 0.998 for STS. Age was found to be a significant factor influencing WFSC properties. The ensemble model (AR) also exhibited comparable prediction accuracy to the SVR-GWO model. In addition, SHAP analysis revealed an optimal content of input variables in the concrete mix. Overall, the hybrid and ensemble models showed exceptional prediction accuracy compared to individual models. The application of these sophisticated soft computing prediction techniques holds the potential to stimulate the widespread adoption of WFS in sustainable concrete production, thereby fostering waste reduction and bolstering the adoption of environmentally conscious construction practices.

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength of expansive soils (PsUCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO (i.e., grey wolf optimization), ANN-SMA (i.e., slime mould algorithm) alongside ANN-MPA (i.e., marine predators’ algorithm) were deployed to assess the PsUCS-ES. The models were trained using the nine most influential parameters affecting PsUCS-ES, collected from a broader range of 145 published papers. The observed results were compared with the predictions made by the ANN-based metaheuristics models. The efficacy of all these formulated models was evaluated by utilizing mean absolute error (MAE), Nash–Sutcliffe (NS) efficiency, performance index ρ, regression coefficient (R2), root mean square error (RMSE), ratio of RMSE to standard deviation of actual observations (RSR), variance account for (VAF), Willmott’s index of agreement (WI), and weighted mean absolute percentage error (WMAPE). All the developed models for Ps-ES had an R significantly > 0.8 for the overall dataset. However, ANN-MPA excelled in yielding high R values for training dataset (TrD), testing dataset (TsD), and validation dataset (VdD). This model also exhibited the lowest MAE of 5.63%, 5.68%, and 5.48% for TrD, TsD, and VdD, respectively. The results of the UCS model’s performance revealed that R exceeded 0.9 in the TrD. However, R decreased for TsD and VdD. Also, the ANN-MPA model yielded higher R values (0.89, 0.93, and 0.94) and comparatively low MAE values (5.11%, 5.67, and 3.61%) in the case of PSO, GWO, and SMA, respectively. The UCS models witnessed an overfitting problem because the aforementioned R values of the metaheuristics were 0.62, 0.56, and 0.58 (TsD), respectively. On the contrary, no significant observation was recorded in the VdD of UCS models. All the ANN-base models were also tested using the a-20 index. For all the formulated models, maximum points were recorded to lie within ± 20% error. The results of sensitivity as well as monotonicity analyses depicted trending results that corroborate the existing literature. Therefore, it can be inferred that the recently built swarm-based ANN models, particularly ANN-MPA, can solve the complexities of tuning the hyperparameters of the ANN-predicted PsUCS-ES that can be replicated in practical scenarios of geoenvironmental engineering.

An improved method for diagnosis of Parkinson’s disease using deep learning models enhanced with metaheuristic algorithm

Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). For accurate diagnosis of PD, powerful machine learning and deep learning models as well as the effectiveness of medical imaging tools for assessing neurological health are required. This study proposes four deep learning models with a hybrid model for the early detection of PD. For the simulation study, two standard datasets are chosen. Further to improve the performance of the models, grey wolf optimization (GWO) is used to automatically fine-tune the hyperparameters of the models. The GWO-VGG16, GWO-DenseNet, GWO-DenseNet + LSTM, GWO-InceptionV3 and GWO-VGG16 + InceptionV3 are applied to the T1,T2-weighted and SPECT DaTscan datasets. All the models performed well and obtained near or above 99% accuracy. The highest accuracy of 99.94% and AUC of 99.99% is achieved by the hybrid model (GWO-VGG16 + InceptionV3) for T1,T2-weighted dataset and 100% accuracy and 99.92% AUC is recorded for GWO-VGG16 + InceptionV3 models using SPECT DaTscan dataset.

Inspection method of the corrosion rate for underwater grouting sleeves by integrating ultrasonic data augmentation and interpretable ensemble learning

To addresses the challenge of underwater grouting sleeve corrosion affecting bridge service life, we propose an innovative inspection approach by integrating a modified ultrasonic testing instrument, ultrasonic data augmentation method, and an improved ensemble learning prediction model. Firstly, the ultrasonic data was collected from grouting sleeve specimens by the modified instrument. Secondly, a data generation model, incorporating Bray Curtis distance and rejection sampling algorithm, was developed to generate samples and filter abnormal data. Then, Circle Chaotic Map improved Grey Wolf Optimizer was proposed to optimize parameters from the Random Forest (RF) algorithm for establishing the prediction model. Finally, SHapley Additive exPlanations (SHAP) method was used to interpret the model globally and locally. Experimental results validate the effectiveness of the data augmentation method, ensuring high-quality ultrasonic data for accurate corrosion rate predictions. The proposed inspection method provide technical support for assessing remaining bridge bearing capacity and service life, showcasing high accuracy, interpretability, and noise tolerance.

A High-Speed Acoustic Echo Canceller Based on Grey Wolf Optimization and Particle Swarm Optimization Algorithms.

Currently, the use of acoustic echo cancellers (AECs) plays a crucial role in IoT applications, such as voice control appliances, hands-free telephony and intelligent voice control devices, among others. Therefore, these IoT devices are mostly controlled by voice commands. However, the performance of these devices is significantly affected by echo noise in real acoustic environments. Despite good results being achieved in terms of echo noise reductions using conventional adaptive filtering based on gradient optimization algorithms, recently, the use of bio-inspired algorithms has attracted significant attention in the science community, since these algorithms exhibit a faster convergence rate when compared with gradient optimization algorithms. To date, several authors have tried to develop high-performance AEC systems to offer high-quality and realistic sound. In this work, we present a new AEC system based on the grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms to guarantee a higher convergence speed compared with previously reported solutions. This improvement potentially allows for high tracking capabilities. This aspect has special relevance in real acoustic environments since it indicates the rate at which noise is reduced.

A machine learning and CFD modeling hybrid approach for predicting real-time heat transfer during cokemaking processes

As the development of green energy and smart industries progresses, concerted efforts are being directed towards the transformation of coking industries. Effective management of heat transfer during the coking process is essential to fortify energy consumption management and augment the quality of coke produced. However, the process during cokemaking is a complex thermochemical conversion process where even minor deviations in operating conditions can lead to instability. Additionally, due to the environmental and chemical conditions inside the coke oven, temperature measurements during operation are difficult. As the fields of Industry 4.0 continue to evolve, numerous industries have begun to leverage intelligent strategies to enhance advanced process management. The digital twin (DG) technique stands as a pivotal technology in the realm of industrial transformation, paving the way for a smarter future. However, given the inherent difficulty in real-time data collection for process operations, the challenge of achieving synchronization becomes increasingly prevalent in the development of DG for the smart coking process. As a result, predicting changes in coal temperature during the cokemaking process is crucial. Currently, computer technology allows for the simulation of complex chemical processes. While some numerical models describe the cokemaking process, these models are limited by the time-consuming characteristic and computing resources. To address this issue, this study proposed a Machine Learning (ML) and Computational Fluid Dynamics (CFD) hybrid model for real-time prediction of heat transfer during the cokemaking process. A CFD model was built based on an industrial-scale coke oven, with cokemaking process data generated through CFD simulation serving as input for the ML models. This study used a total of nine ML models, including ensemble and deep learning models. The hyperparameters of each model were optimized by Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and JAYA algorithms to identify the optimal model structure. Both simulation and empirical experimentation using a coke oven, coupled with industrial data verification, have substantiated the superiority of the proposed model. It simulates the heat transfer during the coking process with high accuracy and efficiency, thereby demonstrating its capacity to significantly reduce energy consumption within the coking industry.

A nutrient optimization method for hydroponic lettuce based on multi-strategy improved grey wolf optimizer algorithm

Hydroponic agriculture, as a modern agricultural technology, not only enables crops to better realize their growth potential but also frees them from the traditional agricultural constraints of land resources. Currently, the nutrient solutions for hydroponic crops are mostly prepared using generic formulas, lacking precise ratios based on the specific needs of different crop types and growth stages. It is of great significance to study the precise nutrient requirements of specific crops at different growth stages to enhance yield. To this end, a multi-strategy improved Grey Wolf Optimization algorithm is proposed in this paper to optimize the fertilizer model for hydroponic lettuce at different growth stages, providing more accurate nutrient requirements for hydroponic lettuce at different growth stages. Firstly, to solve the problems of the traditional GWO algorithm, such as insufficient exploration and the tendency to fall into local optima, non-linear functions, Symbiotic Organisms Search (SOS), and Opposition-Based Learning (OBL) were integrated into the GWO algorithm. Secondly, the objective was to minimize the residual function of the nitrogen, potassium, and calcium ternary fertilizer effect equation, the proposed algorithm was used to optimize the coefficients of the equation. Lastly, the optimal concentrations of NO3−, K+, and Ca2+ for each growth stage of lettuce were calculated based on the optimized fertilizer effect equations. The proposed algorithm was compared with five other excellent metaheuristic algorithms in optimizing fertilizer model coefficients. Experimental results indicate that the proposed algorithm outperforms the other algorithms used in the experiments. For the nutrient models of three growth stages, the proposed algorithm achieves improvements compared to the GWO algorithm, with R2 increasing by 0.73 %, 0.66 %, and 0.51 %, and RMSE decreasing by 8.53 %, 5.70 %, and 3.83 % respectively. This contributes to providing more accurate nutrient requirements for hydroponic lettuce.

A new industrially magnetic capsule MedRobot integrated with smart motion controller

Capsule medical robots with unique application advantages have broad prospects for gastrointestinal detection, targeted drug delivery, medical assistance, and other fields. However, due to the complexity of the gastrointestinal environment and the limitations of the space magnetic field, robust motion control of capsule robots is still a challenge. Using a permanent magnet (NdFeB) as a space magnetic source, a capsule robot manipulation instrument with an integrated motion intelligent control approach is proposed in this work. First of all, a steering fixture controlling a permanent magnet is designed, and a manipulation instrument capable of five degrees of motion freedom is built with low cost and high accuracy. Furthermore, an integrated motion control approach, consisting of a primary motion subsystem and an auxiliary motion subsystem, is presented, where the motion route of the capsule robot is planned. A parallel optimization strategy is adopted to improve the traditional Gray Wolf Optimization Algorithm, the improved version of which is utilized to tune the parameters of controllers. Finally, some experiments of the designed capsule robot are carried out in different planes and slopes, as well as simulated stomach and real pig stomach, respectively. The results show that the motion characteristics are continuous and stable, and the average position error is 4.2 mm, which meets the application requirements.

Optimal hybrid resonant current controller for microgrids connected to an unbalanced IEEE test distribution network

Microgrids (MGs) based on renewable energies have emerged as a proficient strategy for tackling power quality issues in conventional distribution networks. Nonetheless, MG systems require a suitable control scheme to supply energy optimally towards the electrical grid. This paper presents an innovative framework for designing hybrid Proportional-Resonant (PR) controllers with Linear Quadratic Regulators (LQR), PR+LQR, which merge relevant properties of PR and LQR controllers. This method simultaneously determines the MG control parameters and the current unbalanced factor generated at the distribution network. We select the traditional IEEE 13-bus test feeder network and place two MGs at strategic locations to validate our approach. Moreover, we use the Grey Wolf Optimizer (GWO) to find control parameters through a reliable fitness function that leads to high-performance microgrids. Finally, we conceive several tests to assess the efficacy of GWO for tuning the hybrid controller and compare the resulting data across distinct realistic operation conditions representing power quality events. So, we choose four case studies considering different renewable energy penetration indexes and power factors and evaluate the effects of the MGs over the distribution grid. We also compare the proposed hybrid PR+LQR controller against closely-related alternatives from the literature and validate its robustness and stability through the disk margin approach and the Nyquist criterion. Our numerical simulations prove that hybrid controllers driven by GWO are highly reliable strategies, yielding an average unbalanced current reduction of 30.03%.

RSM and ANN Comparative Modelling with a Granulation Treatment in Mixed Waters

AbstractA Box‐Behnken design was used for the analysis using a gray wolf optimizer (GWO)‐coupled artificial neural network (ANN) model and response surface methodology (RSM) to analyze the effect of three operating parameters (volumetric exchange ratio [VER], aeration rate [AR], and cycle time [CT]) manipulated during an aerobic granular sludge process (AGS) sequencing batch reactor on modeling the removal of chemical oxygen demand (COD) in mixed wastewater. The most efficient architecture for COD showed the highest efficiency for modeling the AGS. The RSM model and plot results indicate that the CT and AR were the most influential on COD removal efficiency. When compared with models with statistical indices, GWO‐ANN demonstrated higher performance compared to RSM.

Li-ion battery state of health prediction through metaheuristic algorithms and genetic programming

Predicting the State of Health (SOH) of the Lithium-ion battery with higher accuracy and reduced cost is a challenging and crucial task for ensuring its reliability and safety. To achieve this, a two-stage prediction framework is proposed to find a concise expression of SOH using the health features of the battery through metaheuristic algorithms and genetic programming (GP).In Stage-I, three conflicting objectives are considered concurrently: the root-mean-square error (RMSE) of SOH prediction, the health features selected, and the expressional complexity of the battery capacity. The wrapper structure is utilized for SOH prediction, where a binary multi-objective grey wolf optimization (Binary MOGWO) algorithm is employed to select features and generate the Pareto set. Genetic programming is used to calculate the SOH value using symbolic regression models. In Stage-II, the final compromise solution is filtered from the Pareto set through decision-making approaches. The relationships between selected features and the capacity of the battery are investigated. The NASA Prognostics Center of Excellence battery dataset is chosen to verify the effectiveness of the proposed framework. The simulation results show that the features found through the framework can provide SOH predictions in the form of symbolic equations with higher accuracy, lower cost, and reduced complexity.

An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems

As a newly proposed optimization algorithm based on the social hierarchy and hunting behavior of gray wolves, grey wolf algorithm (GWO) has gradually become a popular method for solving the optimization problems in various engineering fields. In order to further improve the convergence speed, solution accuracy, and local minima escaping ability of the traditional GWO algorithm, this work proposes a multi-strategy fusion improved gray wolf optimization (IGWO) algorithm. First, the initial population is optimized using the lens imaging reverse learning algorithm for laying the foundation for global search. Second, a nonlinear control parameter convergence strategy based on cosine variation is proposed to coordinate the global exploration and local exploitation ability of the algorithm. Finally, inspired by the tunicate swarm algorithm (TSA) and the particle swarm algorithm (PSO), a nonlinear tuning strategy for the parameters, and a correction based on the individual historical optimal positions and the global optimal positions are added in the position update equations to speed up the convergence of the algorithm. The proposed algorithm is assessed using 23 benchmark test problems, 15 CEC2014 test problems, and 2 well-known constraint engineering problems. The results show that the proposed IGWO has a balanced E&P capability in coping with global optimization as analyzed by the Wilcoxon rank sum and Friedman tests, and has a clear advantage over other state-of-the-art algorithms.

Multi-Objective Plum Tree Algorithm and Machine Learning for Heating and Cooling Load Prediction

The prediction of heating and cooling loads using machine learning algorithms has been considered frequently in the research literature. However, many of the studies considered the default values of the hyperparameters. This manuscript addresses both the selection of the best regressor and the tuning of the hyperparameter values using a novel nature-inspired algorithm, namely, the Multi-Objective Plum Tree Algorithm. The two objectives that were optimized were the averages of the heating and cooling predictions. The three algorithms that were compared were the Extra Trees Regressor, the Gradient Boosting Regressor, and the Random Forest Regressor of the sklearn machine learning Python library. We considered five hyperparameters which were configurable for each of the three regressors. The solutions were ranked using the MOORA method. The Multi-Objective Plum Tree Algorithm returned a root mean square error value for heating equal to 0.035719 and a root mean square error for cooling equal to 0.076197. The results are comparable to the ones obtained using standard multi-objective algorithms such as the Multi-Objective Grey Wolf Optimizer, Multi-Objective Particle Swarm Optimization, and NSGA-II. The results are also performant concerning the previous studies, which considered the same experimental dataset.