Manta Ray Foraging Optimization Algorithm Research Articles

Hybridization of Manta-Ray Foraging Optimization Algorithm with Pseudo Parameter-Based Genetic Algorithm for Dealing Optimization Problems and Unit Commitment Problem

The manta ray foraging optimization algorithm (MRFO) is one of the promised meta-heuristic optimization algorithms. However, it can stick to a local minimum, consuming iterations without reaching the optimum solution. So, this paper proposes a hybridization between MRFO, and the genetic algorithm (GA) based on a pseudo parameter; where the GA can help MRFO to escape from falling into the local minimum. It is called a pseudo genetic algorithm with manta-ray foraging optimization (PGA-MRFO). The proposed algorithm is not a classical hybridization between MRFO and GA, wherein the classical hybridization consumes time in the search process as each algorithm is applied to all system variables. In addition, the classical hybridization results in an extended search algorithm, especially in systems with many variables. The PGA-MRFO hybridizes the pseudo-parameter-based GA and the MRFO algorithm to produce a more efficient algorithm that combines the advantages of both algorithms without getting stuck in a local minimum or taking a long time in the calculations. The pseudo parameter enables the GA to be applied to a specific number of variables and not to all system variables leading to reduce the computation time and burden. Also, the proposed algorithm used an approximation for the gradient of the objective function, which leads to dispensing derivatives calculations. Besides, PGA-MRFO depends on the pseudo inverse of non-square matrices, which saves calculations time; where the dependence on the pseudo inverse gives the algorithm more flexibility to deal with square and non-square systems. The proposed algorithm will be tested on the test functions that the main MRFO failed to find their optimum solution to prove its capability and efficiency. In addition, it will be applied to solve the unit commitment (UC) problem as one of the vital power system problems to show the validity of the proposed algorithm in practical applications. Finally, several analyses will be applied to the proposed algorithm to illustrate its effectiveness and reliability.

RETRACTED: Multi-objective economic environmental energy management microgrid using hybrid energy storage implementing and developed Manta Ray Foraging Optimization Algorithm

Different renewable energy sources, active loads, electric vehicles, and storage devices make an independent microgrid (MG). Energy management of MG is an important issue. In this paper, efficient energy management (EM) of a microgrid using a novel method based on a new meta heuristic algorithm is proposed. Proposed MGs include renewable energy resources (wind turbine, fuel cell, and solar cell), plug-in hybrid electric vehicles (PHEVs), and liquid air energy storage (LAES) combined with high-temperature thermal energy storage (HTES). In order to carefully investigate and improve the management method, uncertainty parameters such as wind speed, solar radiation, load demand, and energy price is modeled using a probabilistic approach based on the point estimate method (2m + 1) and the developed manta ray foraging optimization (DMRFO) algorithm for posture forecasting with an uncertainty-weighted measurement error of feature objective is used to solve the EM algorithm. The multi-objective optimization problem presents an improved energy management method considering cost minimization and pollution as objective functions. Afterward, the function of the proposed algorithm is compared with that of other methods and, then, the superiority of the proposed method is confirmed. Finally, it is proved that by using the proposed model, total cost and emission is reduced about 3.5% and 21.33%, respectively.

Reducing fuel cost and enhancing the resource utilization rate in energy economic load dispatch problem

This study contributes for solving the economic load dispatch (ELD) problem to reduce the energy waste caused by thermal generation units and promotes cleaner and sustainable production in the power industry. Electricity is produced by thermal power plants; however, thermal power generation involves low economic benefits and high pollution levels, which hinders cleaner and sustainable production in the power industry. An improved manta ray foraging optimization (IMRFO) algorithm is developed for solving the ELD problem and realizing the cleaner and economic goal of the thermal units. The characteristics of the novel method present that: (1) Sine and cosine adaptations were introduced in the manta ray foraging optimization algorithm to enhance its adaptive ability; (2) a nonlinear convergence factor was presented to enhance the convergence speed; and (3) a differential evolution algorithm was introduced in the original algorithm to enhance robustness. Three typical ELD test systems were selected to prove the feasibility of the IMRFO-based solution method. The results indicated that IMRFO algorithm obtained the most competitive scheduling strategy compared with the existing methods. Improving the economy of power system operation is beneficial to realize cleaner and sustainable power production.

Manta Ray Foraging Optimization with Vector Quantization Based Microarray Image Compression Technique.

DNA microarray technologies enable the analysis of the expression of numerous genes in an individual experiment and become an important approach in the field of medicine and biology for investing genetic function, regulation, and interaction. Microarray images can be investigated well for obtaining the contained genetic data. But is it undesirable to retain the genetic data and avoid the microarray images? Due to considerable attention to DNA microarray and several experiments being performed under distinct conditions, a massive quantity of data gets produced over the globe. In order to store and share the microarray images, effective storage and communication models are needed in a natural way. Vector quantization (VQ) is a commonly utilized tool for compressing images, which mainly aims to produce effective codebooks comprising a collection of codewords. Therefore, this paper presents a manta ray foraging optimization (MRFO) with Linde–Buzo–Gray (LBG) based microarray image compression (MRFOLBG-MIC) technique. The LBG model is commonly utilized to design local optimal codebooks to compress images. The construction of codebooks can be defined as a nondeterministic polynomial time (NP) hard problem and can be resolved by the MRFO algorithm. The codebooks produced from LBG-VQ are optimized using the MRFO algorithm to attain optimum optimal codebooks. When the codebooks are produced by the MRFOLBG-MIC algorithm, Deflate model can be applied to compress the index tables. The design of the MRFO algorithm with LBG and Deflate based index table compression demonstrate the novelty of the work. For demonstrating the enhanced compression efficacy of the MRFOLBG-MIC model, a wide-ranging experimental validation process is performed using a benchmark dataset. The experimental outcomes inferred that the MRFOLBG-MIC model accomplished superior outcomes over the other existing models.

Manta ray foraging optimization algorithm and hybrid Taguchi salp swarm-Nelder–Mead algorithm for the structural design of engineering components

Abstract The adaptability of metaheuristics is proliferating rapidly for optimizing engineering designs and structures. The imperative need for the fuel-efficient design of vehicles with lightweight structures is also a soaring demand raised by the different industries. This research contributes to both areas by using both the hybrid Taguchi salp swarm algorithm-Nelder–Mead (HTSSA-NM) and the manta ray foraging optimization (MRFO) algorithm to optimize the structure and shape of the automobile brake pedal. The results of HTSSA-NM and MRFO are compared with some well-established metaheuristics such as horse herd optimization algorithm, black widow optimization algorithm, squirrel search algorithm, and Harris Hawks optimization algorithm to verify its performance. It is observed that HTSSA-NM is robust and superior in terms of optimizing shape with the least mass of the engineering structures. Also, HTSSA-NM realize the best value for the present problem compared to the rest of the optimizer.

Optimal Design of Passive Power Filters Using the MRFO Algorithm and a Practical Harmonic Analysis Approach including Uncertainties in Distribution Networks

The design of Passive Power Filters (PPFs) has been widely acknowledged as an optimization problem. This paper addresses the PPF parameters design problem using the novel Manta Ray Foraging Optimization (MRFO) algorithm. Moreover, an analytical method based on Monte Carlo Simulation (MCS) is proposed to investigate the harmonic performance of such an optimally designed PPF with variations in power networks. The MRFO algorithm has shown a superior solution-finding ability, but a relatively higher computational effort in comparison with other recently proposed algorithms. The harmonic performance of the optimal PPF solution with uncertainties was analyzed using the proposed method. The results imply that the optimally designed PPF can effectively attenuate the high-order harmonics and improved the system performance parameters over different operating conditions to continually comply with the standard limits. The proposed MCS method showed that the optimally designed PPF reduced the voltage and current distortions by roughly 54% and 30%, respectively, and improved the network hosting capacity by 10% for the worst-case scenario.

An enhanced manta ray foraging optimization algorithm for shape optimization of complex CCG-Ball curves

The shape optimization of complex curves is a crucial and intractable technique in computer aided geometric design and widely used in many product design and manufacturing fields involving complex curve modeling. In this paper, an enhanced manta ray foraging optimization (MRFO) algorithm is used to optimize the shape of complex composite cubic generalized Ball (CCG-Ball, for short) curves. Firstly, to solve the problems of shape optimization for Ball curves, we construct a class of new cubic generalized Ball basis, and then present the CCG-Ball curves with multiple shape parameters based on the constructed basis functions. The shapes of the curves can be modified and optimized easily by using the shape parameters. Secondly, the shape optimization of CCG-Ball curves is mathematically an optimization problem that can be efficiently dealt with by swarm intelligence algorithm. In this regard, an enhanced MRFO called WMQIMRFO algorithm, combined with control parameter adjustment, wavelet mutation and quadratic interpolation strategy, is developed to enhance its capability of jumping out of the local minima and improve the calculation accuracy of the native algorithm. Furthermore, the superiority of the WMQIMRFO algorithm is verified by comparing with standard MRFO, other improved MRFO and popular nature-inspired optimization algorithms on the well-known CEC’14 and CEC’17 test suite as well as four engineering optimization problems, respectively. Finally, by minimizing the bending energy of the CCG-Ball curves as the evaluation standard, the shape optimization models of the curves with 1th-order and 2th-order geometric continuity are established, respectively. The WMQIMRFO algorithm is utilized to solve the established models, and the CCG-Ball curves with minimum energy are obtained. Some representative numerical examples illustrate the ability of the proposed WMQIMRFO algorithm in effectively solving the shape optimization problems of complex CCG-Ball curves in terms of precision, robustness, and convergence characteristics.

Recognition of Key Information in Non-Stationary Signals Based on Wavelet Threshold Denoising and Back Propagation Neural Network Optimized by Manta Ray Foraging Optimization Algorithm

The identification of key information hidden in non-stationary signals is challenging in various fields such as logistics and transportation, biomedicine, and fault diagnosis. To facilitate this identification, we propose a back propagation neural network (BPNN) classification and recognition algorithm based on wavelet threshold denoising (WTD) and manta ray foraging optimization (MRFO) algorithm for the first time. The algorithm first performs WTD on the original signals to obtain denoised signals. Subsequently, the MRFO algorithm is utilized to optimize the initial weights and thresholds of the BPNN. On the base of this, the optimization model is finally obtained to classify and recognize the key information in the non-stationary signals. The comparative experimental results indicate that the proposed WTD-MRFO-BPNN algorithm can be utilized to availably recognize the key information hidden in non-stationary signals. The recognition accuracy reaches 97.25%.

Energy Aware Metaheuristic Optimization with Location Aided Routing Protocol for MANET

A mobile ad hoc network (MANET) involves a group of wireless mobile nodes which create an impermanent network with no central authority and infrastructure. The nodes in the MANET are highly mobile and it results in adequate network topology, link loss, and increase the re-initialization of the route discovery process. Route planning in MANET is a multi-hop communication process due to the restricted transmission range of the nodes. Location aided routing (LAR) is one of the effective routing protocols in MANET which suffers from the issue of high energy consumption. Though few research works have focused on resolving energy consumption problem in LAR, energy efficiency still remains a major design issue. In this aspect, this study introduces an energy aware metaheuristic optimization with LAR (EAMO-LAR) protocol for MANETs. The EAMO-LAR protocol makes use of manta ray foraging optimization algorithm (MRFO) to help the searching process for the individual solution to be passed to the LAR protocol. The fitness value of the created solutions is determined next to pass the solutions to the objective function. The MRFO algorithm is incorporated into the LAR protocol in the EAMO-LAR protocol to reduce the desired energy utilization. To ensure the improved routing efficiency of the proposed EAMO-LAR protocol, a series of simulations take place. The resultant experimental values pointed out the supreme outcome of the EAMO-LAR protocol over the recently compared methods. The resultant values demonstrated that the EAMO-LAR protocol has accomplished effectual results over the other existing techniques.

Hard-Rock TBM Thrust Prediction Using an Improved Two-Hidden-Layer Extreme Learning Machine

It is difficult for tunnel boring machine (TBM) operators to respond for safe and high-efficient construction without accurate reference parameters such as the TBM thrust. A new hybrid model (MRFO-AT-TELM) combining an improved two-hidden-layer extreme learning machine (AT-TELM) and manta ray foraging optimization (MRFO) algorithm is proposed to predict TBM thrust with 12 selected input featuring parameters. The affine transformation (AT) activation function is used to improve the performance of TELM. Input weights and bias of AT-TELM are optimized using the MRFO algorithm. The performance of the proposed model is validated with TBM construction data collected from the Yin-Song Project in China and compared with other models. Input data of the first 30, 60, and 90 seconds of the rising period are analyzed. Results show that the proposed model is superior to the other models and with 90-second data as input outperforms that with 30 and 60-seconds data. The proposed model and the selected input features are validated in a new project. The thrust prediction model can be embedded into the TBM construction intelligence system and thus help improve construction efficiency.

Manta Ray Foraging Optimization with Machine Learning Based Biomedical Data Classification

The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various sources. The developments of artificial intelligence (AI) and machine learning (ML) models assist in the effectual design of medical data classification models. Therefore, this article concentrates on the development of optimal Stacked Long Short Term Memory Sequence-to-Sequence Autoencoder (OSAE-LSTM) model for biomedical data classification. The presented OSAE-LSTM model intends to classify the biomedical data for the existence of diseases. Primarily, the OSAE-LSTM model involves min-max normalization based pre-processing to scale the data into uniform format. Followed by, the SAE-LSTM model is utilized for the detection and classification of diseases in biomedical data. At last, manta ray foraging optimization (MRFO) algorithm has been employed for hyperparameter optimization process. The utilization of MRFO algorithm assists in optimal selection of hypermeters involved in the SAE-LSTM model. The simulation analysis of the OSAE-LSTM model has been tested using a set of benchmark medical datasets and the results reported the improvements of the OSAE-LSTM model over the other approaches under several dimensions.

Association rule mining based fuzzy manta ray foraging optimization algorithm for frequent itemset generation from social media

AbstractNowadays, the concept of data mining is employed widely and created a great deal of attention due to its fast arrival. Numerous approaches to frequent itemsets and association rule mining (ARM) are exemplified in recent years, but still, the performances based on scalability and processing time are considered as a major drawback that results in obtaining the solutions with very poor quality. To overcome such shortcomings, this article proposes three significant phases, namely, the data pre‐processing phase, data pre‐processing, frequent itemset mining, and ARM. In data pre‐processing phase, the collected twitter datasets are pre‐processed to eliminate redundant data and convert them into an appropriate format for further mining. In the frequent itemset mining phase, an Apriori algorithm is employed for the exact mining of frequent itemsets. The ARM phase utilizes the fuzzy manta ray foraging (FMRF) optimization algorithm that involves the generation of association rules from the huge itemsets thereby achieving minimum confidence and minimum support value. Here, the recent tweets regarding Covid‐19, trump2020, joebiden, draintheswamp, and Godzilla are the datasets collected from the Twitter web link. The experimental analysis and the comparative performances are performed for various simulation measures and the results reveal that the proposed approach provides effective performances when compared with various other existing approaches.

Energy Loss Reduction of Distribution Systems Equipped with Multiple Distributed Generations Considering Uncertainty using Manta-Ray Foraging Optimization

This paper has adopted the new bio-inspired Manta-Ray Foraging Optimization (MRFO) algorithm for optimal allocation of multiple Distributed Generation (DG) units attached to Radial Distribution Systems (RDSs) in order to reduce the total energy loss of the studied system. The DG units are optimized to work with a unity power factor (UPF) and optimal power factor (OPF) during a 24-h time-varying demand. The MRFO algorithm optimized single, two, and three DG units. The total energy loss and energy-saving during the time-varying demand are calculated and compared with the original case. The MRFO algorithm behavior is compared to the Particle Swarm Optimization (PSO) and Atom Search Optimization (ASO) algorithms regarding energy loss and energy-saving values. The standard 69-bus RDS is used as a test system. Considerable improvements in energy saving, loss reduction, and voltage profile are achieved after installing DG units, mainly when operating with optimal power factors. The MRFO algorithm achieves energy losses of 817.91, 751.08, and 730.25 kWh with 1, 2, and 3 DG units with UPF allocations, respectively. On the other hand, when the DG units are optimized to work with OPF, the MRFO achieves energy losses of 233.24, 142.08, and 106.79 kWh with the same number of DG units, respectively. Furthermore, the MRFO algorithm has efficient behavior compared with the PSO, ASO, and other algorithms for different operations and conditions.

Energy Loss Reduction of Distribution Systems Equipped with Multiple Distributed Generations Considering Uncertainty using Manta-Ray Foraging Optimization

This paper has adopted the new bio-inspired Manta-Ray Foraging Optimization (MRFO) algorithm for optimal allocation of multiple Distributed Generation (DG) units attached to Radial Distribution Systems (RDSs) in order to reduce the total energy loss of the studied system. The DG units are optimized to work with a unity power factor (UPF) and optimal power factor (OPF) during a 24-h time-varying demand. The MRFO algorithm optimized single, two, and three DG units. The total energy loss and energy-saving during the time-varying demand are calculated and compared with the original case. The MRFO algorithm behavior is compared to the Particle Swarm Optimization (PSO) and Atom Search Optimization (ASO) algorithms regarding energy loss and energy-saving values. The standard 69-bus RDS is used as a test system. Considerable improvements in energy saving, loss reduction, and voltage profile are achieved after installing DG units, mainly when operating with optimal power factors. The MRFO algorithm achieves energy losses of 817.91, 751.08, and 730.25 kWh with 1, 2, and 3 DG units with UPF allocations, respectively. On the other hand, when the DG units are optimized to work with OPF, the MRFO achieves energy losses of 233.24, 142.08, and 106.79 kWh with the same number of DG units, respectively. Furthermore, the MRFO algorithm has efficient behavior compared with the PSO, ASO, and other algorithms for different operations and conditions.

Novel technique for the interpretation of gravity anomalies over geologic structures with idealized geometries using the Manta ray foraging optimization

In this study, the potential and efficacy of adapting the bioinspired Manta ray foraging optimization algorithm (MRFO) as a tool for estimating model parameters of two-dimensional gravity profile anomalies is presented. The process of implementing the MRFO algorithm for accurate estimation of shape/depth defining parameters over geometric geologic structures was assessed. The experimental data comprised synthetically generated gravity anomalies that were later corrupted with white Gaussian noise at levels of 5, 10, and 15%, and then, case examples were taken from mining sites across different parts of the world. The algorithm was found to be fast, stable, and consistent in its search for the global best solution to each of the geophysical inverse problems. Its performance was excellent when confronted with constrained multi-parameter non-linear inversion problems and exhibited admirable stability even in the presence of noise. The consistency of the estimated results, when compared to actual values, affirmed the reliability of the procedure. It is, therefore, a stable and efficient tool for performing geophysical data inversion and is recommended for use in inverting geophysical data with higher complexities like seismic reflection, self-potential and magnetic data, which require many corrections to be performed before reliable geological interpretations can be made.

Optimal management of static volt‐ampere‐reactive devices and distributed generations with reconfiguration capability in active distribution networks

Recently, power system planners and operators are of interest to maximize distribution automation investments. Optimal operation of control centers of fully automated distribution systems enables it to control all switches as well as dispatching connected controllable apparatuses. Most previously enhanced distribution systems, which include additional enhancement apparatuses, such as distribution static VAr compensators, capacitor banks, and distributed generators, cannot take advantage of the features of these apparatuses because there is no efficient and reliable control. Accordingly, this article presents a robust and efficient technique using Manta ray foraging optimization algorithm (MRFOA) for optimal coordination and control of such apparatuses as well as distribution network reconfiguration simultaneously. The proposed procedure is employed for the dynamic operation of automated distribution systems, considering daily load variations, for losses minimization and emissions reduction with diverse cases of both the 33-bus and 69-bus distribution test systems. To manifest, these cases are developed to validate the proposed technique and to achieve optimum control and operation of the distribution systems. Despite the complexity of this problem, the proposed technique can achieve the optimal solution for the various considered cases. As well, the comparative assessment of the proposed MRFOA with relevant techniques demonstrates its effectiveness as an effective tool used in the control centers of modern automated distribution systems. One of the most complex challenges to distribution systems operators in the practical daily load variations is the control and optimal operation of the associated dispatchable enhancement apparatuses such as DGs and DSVCs. The DNR makes the utilization and control of such aforementioned enhancement apparatuses more feasible for distribution systems, but this is in return for increasing the complexity of the problem. Significant technical and environmental benefits have been achieved. Great power losses reduction (about 80%) and voltage enhancement are achieved using the MRFOA. In addition, the emission levels are kept at the lowest levels.

Swarm Intelligence-Based Feature Selection for Amphetamine-Type Stimulants (ATS) Drug 3D Molecular Structure Classification

ABSTRACT Swarm intelligence-based feature selection techniques are implemented by this work to increase classifier performance in classifying Amphetamine-type Stimulants (ATS) drugs. A recently proposed 3D Exact Legendre Moment Invariants (3D-ELMI) molecular descriptors as 3D molecular structure representational for ATS drugs. These descriptors are utilized as the dataset in this study. However, a large number of descriptors may cause performance degradation in the classifier. To complement this issue, this research applies three swarm algorithms with k-Nearest Neighbor (k-NN) classifier in the wrapper feature selection technique to ensure only relevant descriptors are selected for the ATS drug classification task. For this purpose, the binary version of swarm algorithms facilitated with the S-shaped or sigmoid transfer function known as binary whale optimization algorithm (BWOA), binary particle swarm optimization algorithm (BPSO), and new binary manta-ray foraging optimization algorithm (BMRFO) are developed for feature selection. Their performance is evaluated and compared based on seven performance criteria. Furthermore, the optimal feature subset was then evaluated with seven different classifiers. Findings from this study have revealed the dominance of BWOA by obtaining the highest classification accuracy with the small feature size.

An analysis of optimal power flow strategies for a power network incorporating stochastic renewable energy resources

An optimal power management solution is a potential tool to produce cost-effective and environmentally friendly power supply using renewable energy sources (RESs) for the electrical power network. Therefore, the article introduces a novel optimization algorithm inspired by the vitality, namely, Manta Ray Foraging Optimization (MRFO), to figure out both multi- and single-objective problems of optimal power flow (OPF) incorporating stochastic RES. The OPF problems are designed by considering four different objective functions: transmission power loss, emission index, fuel operational costs, and voltage deviation. The stochastic and volatile nature of RES increases the complexity of the OPF issue. In this study, a new MRFO algorithm and some modern metaheuristic algorithms were used to settle the issue of OPF, enhance the energy efficiency, and environmental and cost performance of the power network. The test cases, with and without RES, different RES locations on the network, increase in the load, and outages of some transmission lines, are considered by addressing the challenge of the proposed OPF. These cases are tested with bus systems as 30 and 118, and the outcome from the suggested MRFO is compared with six metaheuristic optimization algorithms. Moreover, OPF challenges are successfully settled by the MRFO algorithm and outperform the proposed metaheuristic optimization methods.

Optimal Allocation and Planning of Distributed Power Generation Resources in a Smart Distribution Network Using the Manta Ray Foraging Optimization Algorithm

In this study, optimal allocation and planning of power generation resources as distributed generation with scheduling capability (DGSC) is presented in a smart environment with the objective of reducing losses and considering enhancing the voltage profile is performed using the manta ray foraging optimization (MRFO) algorithm. The DGSC refers to resources that can be scheduled and their generation can be determined based on network requirements. The main purpose of this study is to schedule and intelligent distribution of the DGSCs in the smart and conventional distribution network to enhance its operation. First, allocation of the DGSCs is done based on weighted coefficient method and then the scheduling of the DGSCs is implemented in the 69-bus distribution network. In this study, the effect of smart network by providing real load in minimizing daily energy losses is compared with the network includes conventional load (estimated load as three-level load). The simulation results cleared that optimal allocation and planning of the DGSCs can be improved the distribution network operation with reducing the power losses and also enhancing the voltage profile. The obtained results confirmed superiority of the MRFO compared with well-known particle swarm optimization (PSO) in the DGSCs allocation. The results also showed that increasing the number of DGSCs reduces more losses and improves more the network voltage profile. The achieved results demonstrated that the energy loss in smart network is less than the network with conventional load. In other words, any error in forecasting load demand leads to non-optimal operating point and more energy losses.

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images.

Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people’s safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO’s initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu’s method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu’s method for all the used metrics.