Biogeography-based Optimization Research Articles

Developing seasonal z-number regression for waste-disposal forecasting in a Taiwanese hospital

This study develops a seasonal z-number regression (SZR) to forecast the daily generated amounts of clinical waste for recycling and related waste. The proposed SZR designs new z-number intervals based on least-squares support-vector regression and is combined with the seasonal-decomposition method. Z-numbers characterize the uncertainty of a variable using a fuzzy number and the associated reliability of this fuzzy number. Biogeography-based optimization is employed to select the parameters of the SZR model. In this study, SZR, long short-term memory, support-vector regression with a genetic algorithm, a generalized regression neural network, the autoregressive integrated moving average, and a recurrent neural network are applied to forecast the daily amounts of generated clinical waste for recycling and related waste. The empirical results indicate the following. First, the SZR model demonstrates better performance and robustness than the other approaches for the prediction of the daily amount of clinical waste for recycling and related waste. Second, the SZR model also exhibits superior performance compared to the other approaches with respect to different testing terms. The proposed SZR method can help experts in hospitals develop reasonable waste-disposal projects that can help waste-management systems effectively achieve operational reliability and economy.

Achieving resilient cities using data-driven energy transition: A statistical examination of energy policy effectiveness and community engagement

In an era where energy usage is increasingly critical due to the growing prevalence of cloud computing data centers, this study delves into the realm of dynamic energy management (DEM) within cloud data centers (CDCs) to foster efficient power utilization. Our research introduces a Dynamic System Management (DSM) framework tailored for CDCs, encompassing a dynamic voltage scaling (DVS) management unit, a load balancing unit, and a task scheduling unit (TSU). The TSU employs a stochastic Petri net-based planning procedure and advances a 'role-based' resource placement approach, optimizing task scheduling for enhanced system performance and energy efficiency. Through comprehensive simulation tests, we validate the proposed system scheme. Furthermore, this paper extends its focus to augment the resilience of energy policies in the context of smart cities and foster community engagement. Building upon the existing Biogeography-Based Optimization (BBO), we propose enhancements to fortify its robustness and capabilities. Notably, our modified BBO approach demonstrates remarkable reductions in power consumption—27%, 32%, and 39% less when compared to BBO, particle swarm optimization, and genetic algorithms, respectively. These findings underscore the pivotal role of data-driven energy transition and community engagement in building resilient cities for the future.

A comparative approach for the optimal design of steel structures using biogeography-based optimization (BBO) algorithm and genetic algorithm (GA)

Structural optimization is one of the key concerns of civil engineering designers, but from a mathematical point of view, this optimization problem is highly complex and complicated due to a large number of non-linear design constraints and the iterative procedure of structural analysis. Introducing optimization algorithms such as biogeography-Based Optimization (BBO) and genetic algorithms (GA) into applications can help the user to optimize the cost of the structure to be adopted more quickly and with fewer errors in the preliminary phase of the design study. The aim of this research is to carry out a comparative approach to structure weight minimization using biogeography-Based Optimization (BBO) algorithm and genetic algorithms (GA), examining the influence of the number of populations and the number of iterations in the final results. In this study, both algorithms gave reliable results, but a comparison of the results obtained by the two methods reveals that the biogeography-Based Optimization algorithm (BBO) can be successfully used for the optimization of steel structures while ensuring verification of the strength, serviceability and stability criteria defined by Eurocode 3 (Union, 2006), as it has certain advantages in detecting the global minimum over genetic algorithms (GA). It is capable of finding solutions that are lighter, stiffer and have lower deflection than the original designs.

An adaptive biogeography-based optimization with integrated covariance matrix learning for robust visual object tracking

An adaptive biogeography-based optimization with integrated covariance matrix learning for robust visual object tracking

FC-PV-battery-Z source-BBO integrated unified power quality conditioner for sensitive load & EV charging station

The proposed integrated system combines of fuel cell (FC), photovoltaic (PV), battery, Z-source and Biogeography-Based Optimization (BBO) based Unified Power Quality Conditioner (UPQC) compensator aims to provide a high-quality power supply to sensitive loads, such as electrical, electronics, medical equipment, Electrical Vehicles (EV), or critical industrial processes. It can regulate voltage, mitigate power disturbances (e.g., voltage sags, swells, flicker, voltage interruption, harmonics, reactive power etc.), and ensure a stable power supply. Additionally, it can facilitate EV charging stations by managing the power flow and optimizing charging processes. The PV or FC based Z-source inverter can control and regulate power flow in both directions. It operates with a unique impedance network, allowing for voltage boosting or bucking. The Z-source inverter enhances the flexibility and control of power flow in the system. The fuel cell generates electricity through an electrochemical process, typically using hydrogen as a fuel source, while the PV array converts sunlight into electrical energy. The FC-PV system provides a renewable and clean energy source. The battery serves as an energy storage device within the integrated UPQC system. It stores excess energy produced by the FC-PV system or from the grid during off-peak hours and releases it when needed, contributing to load balancing and stability. Biogeography-Based Optimization (BBO)-based Selected Harmonic Elimination Pulse Width Modulation (SHE-PWM) technique calculate the proper inverter switching angles and control the total harmonic distortion (%THD). Grid connected EV charging station produce nonlinear current and pollute the DC-link system. The proposed FC-PV-Z source-BBO based UPQC and its control strategy can enhance the EV-charging induced PQ (power quality) issues, eliminate the voltage and current harmonic or reduce overall voltage and current %THD control the proper reactive power, improve the power factor, restoring the flicker, transient, swells and sags of the micro grid, grid or smart grid and completely moderate the fluctuations of DC-link voltage.

A Feature Selection Algorithm Based on Differential Evolution for English Speech Emotion Recognition

The automatic identification of emotions from speech holds significance in facilitating interactions between humans and machines. To improve the recognition accuracy of speech emotion, we extract mel-frequency cepstral coefficients (MFCCs) and pitch features from raw signals, and an improved differential evolution (DE) algorithm is utilized for feature selection based on K-nearest neighbor (KNN) and random forest (RF) classifiers. The proposed multivariate DE (MDE) adopts three mutation strategies to solve the slow convergence of the classical DE and maintain population diversity, and employs a jumping method to avoid falling into local traps. The simulations are conducted on four public English speech emotion datasets: eNTERFACE05, Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Surrey Audio-Visual Expressed Emotion (SAEE), and Toronto Emotional Speech Set (TESS), and they cover a diverse range of emotions. The MDE algorithm is compared with PSO-assisted biogeography-based optimization (BBO_PSO), DE, and the sine cosine algorithm (SCA) on emotion recognition error, number of selected features, and running time. From the results obtained, MDE obtains the errors of 0.5270, 0.5044, 0.4490, and 0.0420 in eNTERFACE05, RAVDESS, SAVEE, and TESS based on the KNN classifier, and the errors of 0.4721, 0.4264, 0.3283 and 0.0114 based on the RF classifier. The proposed algorithm demonstrates excellent performance in emotion recognition accuracy, and it finds meaningful acoustic features from MFCCs and pitch.

Building energy consumption prediction and optimization using different neural network-assisted models; comparison of different networks and optimization algorithms

The consumption of energy in buildings holds considerable importance within the realm of overall energy usage. This underscores the critical nature of employing efficient strategies for managing energy. Accurate assessments of energy consumption in buildings serve as a central factor in improving energy efficiency and providing guidance for energy management choices in the context of residential buildings. Therefore, predicting and optimizing building energy utilization has become a popular area of research because it has the potential to greatly improve how efficiently energy is used in buildings. In this study, in the first step, four different models, including three artificial neural network frameworks and a regression model are expanded to predict cooling and heating loads. After selecting the best network, using four optimization techniques, the hyperparameters of the selected network are tuned and the best hybrid model is obtained. Furthermore, the multi-objective optimization process is extended to define the optimal conditions using Particle Swarm Optimization, and Biogeography-Based Optimization optimizers and LINMAP, TOPSIS decision-making approaches. The findings of this investigation underscore the enhanced efficiency conferred by the BBO algorithm on the Extreme Learning Machine. Specifically, an increase in the correlation coefficient from 0.9959 to 0.9969 for cooling load estimation and from 0.9973 to 0.9993 for heating load estimation reflects the improved alignment of results from the ELM-BBO model with actual experimental data. These values surpass those of all other models, indicating that the ELM-BBO model demonstrates the best performance among the hybrid models. Importantly, the results obtained underscore the overall effectiveness of the selected optimizers in delivering accurate outcomes.

A crossover-based multi-objective discrete particle swarm optimization model for solving multi-modal routing problems

Passengers must use a set of modes and vehicles to reach their destination in complicated urban structures. Choosing an optimal route is a complicated optimization problem for these passengers. This study proposes a multi-objective optimization algorithm to solve the routing problem in the urban multi-modal network. The multi-modal network problem considered in this study is a transportation network with subway, Bus Rapid Transit (BRT), taxi, and walking modes. The objective functions determine the optimized route by considering route length, traffic, comfort, and safety. We develop a Crossover-Based Multi-Objective Discrete Particle Swarm Optimization (CBMODPSO) to solve the problem. CBMODPSO has been improved using mutation and crossover operators. Multi-Objective Artificial Bee Colony (MOABC), Multi-Objective Ant Colony Optimization (MOACO), Multi-Objective Biogeography-Based Optimization (MOBBO), Multi-Objective Gray Wolf Optimization (MOGWO), and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithms are used to evaluate and compare the results from the CBMODPSO algorithm. In addition, CBMODPSO results are compared with previous research results. We show the improved algorithm is more repeatable than other algorithms. The CBMODPSO algorithm has a faster convergence rate and is able to get to an optimal solution in a smaller generation number and much less time. The CBMODPSO algorithm is implemented in about one-thirties of the MOBBO duration. Meanwhile, it has a reproducibility of almost twice the MOGWO algorithm.

A biogeography-based optimization algorithm with modified migration operator for large-scale distributed scheduling with transportation time

To expand production scale and enhance enterprise competitiveness, modern supply chains and manufacturing systems have shifted from the single factory production to the multi-factory production network with the characteristics of large scale, diversification of varieties, redundant production factories, flexible production processes, and high-value products. This production mode is called distributed scheduling. The transportation time caused by geographically distributed factories in Distributed Scheduling Problem (DSP) with a series-parallel network plays an important role in the scheduling scheme. To minimize the global makespan over all factories, a two-segment representation has been taken as an encoding scheme while the decoding procedure applies the First Come First Served (FCFS) heuristic rule. A Biogeography-Based Optimization with Modified Migration Operator (BBO-MMO) is then proposed to address the heterogeneous DSP with transportation time between factories at adjacent processing stages, which improves the migration operator by modifying the immigration rate according to processing time and transportation time. A cosine migration model has been adopted to enhance the ability of BBO-MMO to break through local optimum. BBO-MMO is compared with the Improved Memetic Algorithm (IMA), and several groups of examples with various parameters have been generated to test the performance of BBO-MMO. The results show that the proposed BBO-MMO can effectively solve the large-scale heterogeneous DSP with transportation time.

A novel neural-evolutionary framework for predicting weight on the bit in drilling operations

This study compares the performance of artificial neural networks (ANN) trained by grey wolf optimization (GWO), biogeography-based optimization (BBO), and Levenberg–Marquardt (LM) to estimate the weight on bit (WOB). To this end, a dataset consisting of drilling depth, drill string rotational speed, rate of penetration, and volumetric flow rate as input variables and the WOB as a response is used to develop and validate the intelligent tools. The relevance test is applied to sort the strength of WOB dependency on the considered features. It was observed that the WOB has the highest linear correlation with the drilling depth and drill string rotational speed. After dividing the databank into the training and testing (4:1) parts, the proposed LM-ANN, GWO-ANN, and BBO-ANN ensembles are constructed. A sensitivity analysis is then carried out to find the most powerful structure of the models. Each model performs to reveal the relationship between the WOB and the mentioned independent factors. The performance of the models is finally evaluated by mean square error (MSE) and mean absolute error criteria. The results showed that both GWO and BBO algorithms effectively help the ANN to achieve a more accurate prediction of the WOB. Accordingly, the training MSEs decreased by 14.62% and 24.90%, respectively, by applying the GWO and BBO evolutionary algorithms. Meanwhile, these values were obtained as around 9.86% and 9.41% for the prediction error of the ANN in the testing phase. It was also deduced that the BBO performs more efficiently than the other technique. The effect of input variables dimension on the accuracy and training time of the BBO-ANN clarified that the most accurate WOB predictions are achieved when the model constructs with all four input variables instead of utilizing either three or two of them with the highest linear correlation. It was also observed that the training stage of the BBO-ANN model with four input variables needs a little more computational time than its training with either two or three variables. Finally, the accuracy of the BBO-ANN model for the WOB prediction has been compared with the multiple linear regression, support vector regression, adaptive neuro-fuzzy inference systems, and group method of data handling. The statistical accuracy analysis confirmed that the BBO-ANN is more accurate than the other checked techniques.

Large-Scale Portfolio Optimization Using Biogeography-Based Optimization

Portfolio optimization is a mathematical formulation whose objective is to maximize returns while minimizing risks. A great deal of improvement in portfolio optimization models has been made, including the addition of practical constraints. As the number of shares traded grows, the problem becomes dimensionally very large. In this paper, we propose the usage of modified biogeography-based optimization to solve the large-scale constrained portfolio optimization. The results indicate the effectiveness of the method used.

Evaluating the rapid chloride permeability of self-compacting concrete containing fly ash and silica fume exposed to different temperatures: An artificial intelligence framework

One of the major known challenges of improving durability of concrete structures is reducing the permeability of concrete to retard the transport of chloride ions. In this regard, the utilization of self-compacting concrete (SCC) incorporating supplementary cementitious materials such as fly ash and silica fume can aid to improve the resistance against chloride ion penetration. With the aim of evaluating the effectiveness of the SCCs, existing experimental methods, such as the rapid chloride permeability test (RCPT), demand time, financial resources, and skilled technicians. This study proposes three accurate and reliable predictive models through the combination of metaheuristic optimization techniques of particle swarm optimization (PSO), ant colony optimization (ACO) and biogeography-based optimization (BBO) with artificial neural network (ANN) in order to minimize the reliance on time-consuming and costly laboratory activities. To this end, 360 RCPT results on 60 SCC mixtures incorporating various percentages of fly ash and silica fume exposed to different temperatures during the RCPT were collected. The performance of the developed models was assessed by different statistical indicators. The results reveal that the proposed ANN-BBO model exhibits a higher accuracy than the single ANN, ANN-PSO and ANN-ACO models. It is also found that the input parameter of the sample temperature during testing is the most important variable in controlling the RCPT of SCC with a contribution of 25%. Comparing the performance of the best proposed model (ANN-BBO) with that of existing models in the literature reveals that the ANN-BBO model has a higher accuracy in predicting the chloride ion penetration resistance of SCCs.

State of Charge Estimation for Power Battery Using Improved Extended Kalman Filter Method Based on Neural Network

In order to enhance the accuracy of the traditional extended Kalman filter (EKF) algorithm in the estimation of the state of charge (SoC) of power batteries, we first derived the state space equation and measurement equation of lithium power batteries based on the Thevenin battery model and the modified Ampere-Hour integralalgorithm. Then, the basic principles of EKF, backpropagation neural networks (BPNNs), and a biogeography-based optimization (BBO) algorithm were analyzed, and the arc curve mobility model was used to improve the global search ability of the BBO algorithm. By combining these three algorithms, this paper proposes a BP neural network method based on the BBO algorithm. This method uses the BBO algorithm to optimize the incipient weight and threshold of the BP neural network and uses this improved neural network to modify the estimated value of the extended Kalman filter algorithm (BBOBP-EKF). Finally, the BBOBP-EKF algorithm, the extended Kalman filter algorithm based on the BP neural network (BP-EKF), and the EKF algorithm are used to estimate the error value of the SOC of a power battery, and according to the experimental data, it was confirmed that the proposed BBOBP-EKF algorithm has been improved compared to other algorithms with respect to each error index term, in which the maximum error is 1% less than that of the BP-EKF algorithm and 2.4% less than that of the EKF algorithm, the minimum error is also the smallest, and the estimation accuracy is improved compared to the traditional algorithms.

Predicting friction capacity of driven piles using new combinations of neural networks and metaheuristic optimization algorithms

Friction capacity is a principal characteristic in designing driven piles. Considering the complexities in analyzing the behavior of piles, many studies have recommended the use of machine learning for this purpose. However, the used methodologies need to be updated and improved with respect to recent computational advances such as the development of optimization algorithms. In this work, three metaheuristic algorithms, namely equilibrium optimizer (EO), biogeography-based optimization (BBO), and salp swarm algorithm (SSA) are deployed to optimize an artificial neural network (ANN) for predicting pile friction capacity based on pile geometry, effective stress, and shear strength. The findings indicate the suitability of the proposed algorithms. More specifically, in the training phase, the ANN supervised by SSA yielded the most accurate results, whereas in the testing phase, the BBO-ANN outperformed the two other models. The calculated mean absolute error, Pearson correlation coefficient, and root mean square error for the models are as follows: 6.0740, 0.9385, and 7.0678 for the EO-ANN, 6.1450, 0.9440, and 6.7343 for the BBO-ANN, and 5.9684, 0.9395, and 7.1322 for the SSA-ANN. It is shown that both SSA-ANN and BBO-ANN can serve as efficient tools for the reliable design of driven piles, providing efficient computational intelligence alternatives to traditional design methods.

Exploiting drone images for forest fire detection using metaheuristics with deep learning model

Forest fires are a global natural calamity causing significant economic damage and loss of lives. Professionals forecast that forest fires would raise in the future because of climate change. Early prediction and identification of fire spread would enhance firefighting and reduce affected zones. Several systems have been advanced to detect fire. Recently, Unmanned Aerial Vehicles (UAVs) can be used for forest fire detection due to their ability, high flexibility, and inexpensive to cover vast areas. But still, they are limited by difficulties like image degradation, small fire size, and background complexity. This study develops an automated Forest Fire Detection using Metaheuristics with Deep Learning (FFDMDL-DI) model. The presented FFDMDL-DI technique exploits the DL concepts on drone images to identify the occurrence of fire. To accomplish this, the FFDMDL-DI technique makes use of the Capsule Network (CapNet) model for feature extraction purposes with a biogeography-based optimization (BBO) algorithm-based hyperparameter optimizer. For accurate forest fire detection, the FFDMDL-DI technique uses a unified deep neural network (DNN) model. Finally, the tree growth optimization (TGO) technique is utilized for the parameter adjustment of the DNN method. To depict the enhanced detection efficiency of the FFDMDL-DI approach, a series of simulations were performed on the FLAME dataset, comprising 6000 samples. The experimental results stated the improvements in the FFDMDL-DI method over other DL models with maximum accuracy of 99.76%.

Performance analysis of PID controller-based metaheuristic optimisation algorithms for BLDC motor

ABSTRACT Today, the use of permanent magnet brushless DC (PMBLDC) motors in vehicles is increasing due to the characteristics of sensorless operation. PMBLDC motor controllers can control the speed and position in the closed-loop feedback system without the need for a position sensor mounted on the shaft. Proportional – Integral – Derivative (PID) controller is one of the most common feedback control algorithms used in PMBLDC motor controllers. Optimizing problems using deterministic methods such as Lagrange and simplex methods requires basic information and complex calculations. Meta-heuristic algorithms are a type of stochastic algorithm that is used to find the optimal solution. Meta-heuristic algorithms are divided into three general categories: evolutionary algorithms, swarm intelligence algorithms, and stochastic algorithms. In this paper, using 14 metaheuristic optimisation algorithms, PID control parameters including settling time, time rise, overshoot, and stability of step response of the mentioned system are optimised. In this paper, 14 meta-heuristic algorithms are simulated and evaluated to optimise the PID coefficients of the controller, including settling time, rising time, excessive increase, and step response stability. The simulation result shows that the genetic algorithm (GA) has the best performance in terms of cost function and biogeography-based optimisation (BBO) in terms of settling time and rising time parameters. Finally, the simulation results are confirmed using experimental results.

Optimal adaptive neuro-fuzzy inference system with biogeography-based optimization for numbers of COVID-19 vaccination prediction

This study attempts to develop the adaptive neuro-fuzzy inference system (ANFIS) with biogeography-based optimization (BBO) (ANFIS-BBO) for a case study of the actual number of COVID-19 vaccinations in a medical center, considering the variables of the date and time of vaccination, the brand of vaccine, and the number of open appointments on the government network platform in Taiwan. The COVID-19 has brought about a great burden on the health and economy of the world since the end of 2019. Many scholars have proposed a prediction model for the number of confirmed cases and deaths. However, there is still a lack of research in the prediction model for mass vaccination. In this study, ANFIS-BBO is developed to predict the number of COVID-19 vaccination, and three other forecasting models, support vector machines (SVM), least-square support vector machines (LSSVM) and general regression neural network (GRNN) are employed for forecasting the same data sets. Empirical results show that the ANFIS-BBO with trapezoidal membership function model can achieve better performance than other methods and provide robust predictions for the actual number of COVID-19 mass vaccination.

An optimized backpropagation neural network models for the prediction of nanomaterials concentration for purification industrial wastewater

In this paper, an optimized backpropagation neural network (BPNN) prediction models have been proposed to estimate the concentration of titanium dioxide (TiO2) nanomaterials required for the removal of Acid Black 24 dye, a type of textile dye, from industrial wastewater. The research gap addressed in this study lies in the investigation of various optimization algorithms for BPNN models and their profound impact on the accuracy of nanomaterial concentration predictions, thus contributing to the advancement of nanotechnology-based wastewater treatment research and its practical applications in industrial settings. Although several studies have predicted the amount of nanomaterial needed to degrade the dyes in industrial wastewater using BPNN, few have investigated the effects of optimized BPNN and how greatly the amount of degradation can vary. Therefore, six different optimization algorithms, including genetic algorithms (GAs), ant colony optimization (ACO), particle swarm optimization (PSO), evaluation strategy (ES), biogeography-based optimization (BBO), and population-based incremental learning algorithm (PBIL), are applied to optimize and evaluate the BPNN models. Experimental data obtained from chemical laboratory experiments are used to train and test the models, with different experiments conducted for various factors like TiO2, H2SO4, NaIO4, K2S2O8, dye, and silver nitrate. The performance of the optimized BPNN models is assessed using metrics such as mean squared error (MSE), root means squared error (RMSE), and R-squared (R2). The results demonstrate the effectiveness of the proposed models, with ACO yielding the best outcomes across all models. The R2 values for the TiO2, H2SO4, NaIO4, and K2S2O8 models after optimization indicate perfect predictions, whereas the R2 values before optimization ranged from 0.888 to 0.984. PSO performs well for most models except for K2S2O8, GA improves most models except for the silver nitrate model, ES enhances only the Dye model, and PBIL does not affect the TiO2 model. Overall, the optimized BPNN models exhibit impressive accuracy in estimating the TiO2 concentration required for removing Acid Black 24 dye from industrial wastewater.

Exploiting Drone Images for Forest Fire Detection using Metaheuristics with Deep Learning Model

<p>Forest fires are a global natural calamity causing significant economic damage and loss of lives. Professionals forecast that forest fires would raise in the future because of climate change. Early prediction and identification of fire spread would enhance firefighting and reduce affected zones. Several systems have been advanced to detect fire. In recent times, Unmanned Aerial Vehicles (UAVs) are used for tackling this issue because of their ability, high flexibility, and their cheap price to cover vast areas during the nighttime or daytime. But still they are limited by difficulties like image degradation, small fire size, and background complexity. This study develops an automated Forest Fire Detection using Metaheuristics with Deep Learning (FFDMDL-DI) model. The presented FFDMDL-DI technique exploits the DL concepts on drone images to identify the occurrence of fire. To accomplish this, the FFDMDL-DI technique makes use of the Capsule Network (CapNet) model for feature extraction purposes with a biogeography-based optimization (BBO) algorithm-based hyperparameter optimizer. For accurate forest fire detection, the FFDMDL-DI technique uses a unified deep neural network (DNN) model. Finally, the tree growth optimization (TGO) technique is utilized for the parameter adjustment of the DNN method. To depict the enhanced detection efficiency of the FFDMDL-DI approach, a series of simulations were performed. The outcomes reported improvements in the FFDMDL-DI method over other DL models.</p>

Performance Enhancement of Elephant Herding Optimization Algorithm Using Modified Update Operators

This research paper presents a modified version of the Elephant Herding Optimization (EHO) algorithm, referred to as the Modified Elephant Herding Optimization (MEHO) algorithm, to enhance its global performance. The focus of this study lies in improving the balance between exploration and exploitation within the algorithm through the modification of two key operators: the matriarch updating operator and the separation updating operator. By reframing the equations governing these operators, the proposed modifications aim to enhance the algorithm’s ability to discover optimal global solutions. The MEHO algorithm is implemented in the MATLAB environment, utilizing MATLAB R2019a. To assess its efficacy, the algorithm is subjected to rigorous testing on various standard benchmark functions. Comparative evaluations are conducted against the original EHO algorithm, as well as other established optimization algorithms, namely the Improved Elephant Herding Optimization (IEHO) algorithm, Particle Swarm Optimization (PSO) algorithm, and Biogeography-Based Optimization (BBO) algorithm. The evaluation metrics primarily focus on the algorithms’ capacity to produce the best global solution for the tested functions. The proposed MEHO algorithm outperformed the other algorithms on 75% of the tested functions, and 62.5% under two specific test scenarios. The findings highlight the effectiveness of the proposed modification in enhancing the global performance of the Elephant Herding Optimization algorithm. Overall, this work contributes to the field of optimization algorithms by presenting a refined version of the EHO algorithm that exhibits improved global search capabilities.