Moth Flame Optimization Algorithm Research Articles

Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation.

The COVID-19 is one of the most significant obstacles that humanity is now facing. The use of computed tomography (CT) images is one method that can be utilized to recognize COVID-19 in early stage. In this study, an upgraded variant of Moth flame optimization algorithm (Es-MFO) is presented by considering a nonlinear self-adaptive parameter and a mathematical principle based on the Fibonacci approach method to achieve a higher level of accuracy in the classification of COVID-19 CT images. The proposed Es-MFO algorithm is evaluated using nineteen different basic benchmark functions, thirty and fifty dimensional IEEE CEC'2017 test functions, and compared the proficiency with a variety of other fundamental optimization techniques as well as MFO variants. Moreover, the suggested Es-MFO algorithm's robustness and durability has been evaluated with tests including the Friedman rank test and the Wilcoxon rank test, as well as a convergence analysis and a diversity analysis. Furthermore, the proposed Es-MFO algorithm resolves three CEC2020 engineering design problems to examine the problem-solving ability of the proposed method. The proposed Es-MFO algorithm is then used to solve the COVID-19 CT image segmentation problem using multi-level thresholding with the help of Otsu's method. Comparison results of the suggested Es-MFO with basic and MFO variants proved the superiority of the newly developed algorithm.

Fusion of powerful features of Harris Hawks with Cuckoo Search algorithm for engineering applications

The High-level synthesis (HLS) is a key step in the digital VLSI circuit design. HLS can be seen in the present study as an important matter of intelligently map a Data Flow Graph (DFG) specification of digital filters to FPGA. In this article, the powerful features of the Harris hawks optimization (HHO) and Cuckoo search (CS) algorithm have improved the performance of the proposed method. This proposed strategy is also known as the GChO algorithm. In GChO, the phase of CS helps to force the HHO to escape from stagnation, to ignore local optima and premature convergence. In addition for HLS, the three retiming-based models were also proposed in the work. The standard pipelining was employed in Model 1. The modified Min-Period Retiming on the multiplier less digital filters was incorporated by Model 2 and led to a significant improvement in clock frequency. Model 3 adopted modified Min-Area Retiming on digital filters and reduced the complexity of the circuit; however these designs therefore offer single parameter-based heuristic solution. This can be resolved by using meta-heuristic approach focused on retiming. The use of meta-heuristic methods and swarm intelligence has been considered as a highly desirable choice when searching the digital block solution space with higher frequency and reduced complexity in HLS problems. The robustness of the GChO method has been verified on IEEE CEC’ 2020 standard benchmark test suites. The paper analyzes that the performance of the proposed GChO algorithm outperforms the Harris hawks optimization (HHO), Moth Flame optimization algorithm, Particle Swarm optimization algorithm, Chimp optimization algorithm for the HLS digital filters. The experimental results show that the proposed Model 2 and 3 for retimed PDR-FIR filter is improved in term of MUF by 7.38% and utilized slices by 37.77%, PRO-DFII IIR retimed filter by 10.54%, and complexity reduced by 29.69%, PRO-LAT-ARF by 41.61% and slice by 11.17% and PRO-LATLAD-IIR improved in term of MUF, slices by 35.69% and 38.095% in relative to the standard retimed filters of various architecture. For the evolutionary GChO-retimed filter, an improvement in MUF by 58.52%, and utilized slices by 49.61%, for PDR-FIR, 48.09%, 80.76% for PRO-DFII-IIR , 52.68%, 54.33% for PRO-LAT-ARF, 27.85% and 18.46% for PRO-LATLAD-IIR are achieved in respect of Model 2 and 3 whereas an improvement in term of MUF by 34.60%, and utilized slices by 37.61%, for PDR-FIR, 40.55%, 39.66% for PRO-DFII-IIR, 40.08%, 31.33% for PRO-LAT-ARF, 26.95%, 24.41% for PRO-LATLAD-IIR are achieved with respect of other meta-heuristic methods.

An Acquisition Based Optimised Crop Recommendation System with Machine Learning Algorithm

The agricultural sector makes a significant economic impact in India. It contributes 19.9% to the national GDP. The prosperity of the country's economy greatly affects the country's progress and the quality of life for Indian citizens. The vast majority of farms still use antiquated methods rather than adopting a data-driven strategy to increase output and earnings. It is considered a cornerstone of India's financial structure. Since achieving independence, increasing output through the implementation of cutting-edge technologies has been a top priority. Such cutting-edge technology is the application of machine learning algorithms to forecast agricultural outcomes such as harvest size, fertilizer requirements, and the effectiveness of specific farming implements. In this research, a model was built using an optimization and an ensemble of methods to improve the precision and consistency of prediction. Classifiers based on Support Vector Machines (SVM), K Nearest Neighbors (KNN), Decision Trees (DT), and Logistic Regression (LR) were competed against those based on voting and stacking in the ensemble technique. With an accuracy of 99.32%, the Moth Flame Optimization (MFO) algorithm was utilized to recommend the best crop to be harvested.

Moth flame optimization for the maximum power point tracking scheme of photovoltaic system under partial shading conditions

The dwindling reserves of fossil fuels have spurred the expansion of photovoltaic power systems, widely regarded as an alluring solution. Yet, a formidable challenge arises when it comes to optimizing the output of PV systems exposed to irregular irradiance stemming from external environmental factors. Consequently, this research endeavors to advocate the use of the Moth Flame Optimization (MFO) algorithm to search for the highest power output of a solar energy harvesting system. The distinctive behavior of moths proves instrumental in thorough searching of the feasible space, mitigating the risk of entrapment in local optima. To evaluate its efficacy, this algorithm’s performance is validated by comparing its outcomes with those of the Butterfly Optimization Algorithm (BOA). Both algorithms are subjected to experimentation to search for the Global Maximum Power Points (GMPPs) of the PV system under two distinct Partial Shading Condition (PSC) scenarios: Case 1 and Case 2. The results indicate that BOA tends to produce outcomes with a broader data dispersion range relative to the mean, unlike MFO. Specifically, for Case 1, the standard deviation values for MFO and BOA are 2.327040E−02 and 5.777913E−02, respectively, while for Case 2, they are 5.0567340E−02 and 8.519362E−02, respectively. Hence, the proposed approach demonstrates faster and more precise convergence.

Consideration of Thomson effect in multi-objective optimization of thermoelectric generator using metaheuristic techniques

This paper proposes the performance estimation of a multi-parameter multi-objective optimization problem of a thermoelectric generator (TEG) considering the Thomson Effect using metaheuristic techniques. The hot and cold side temperatures, load resistance, length of thermoelectric legs, and fill factor of the TEG are varied simultaneously to optimize the output power and efficiency of the TEG. Thirteen-metaheuristic algorithms have been used to perform single and multi-objective optimization of TEG system with multi-variable space and obtained the best performing algorithm among them for TEG model with and without considering the Thomson effect. In terms of accuracy and speed, moth flame optimization (MFO) and cuckoo search (CS) algorithms are found to be the most suitable for the models with and without considering the Thomson effect. Cuckoo search, genetic algorithm (GA), and moth flame optimization (MFO) algorithms gave consistent results for the model considering the Thomson effect. The TEG performance of both models with and without considering the Thomson effect is also compared. For single-objective optimization, CS and MFO produced optimal power output and efficiency of 16.99325 W & 2.0696 % and 19.20837 W & 2.4427 % respectively for models with and without considering the Thomson effect respectively. For multi-objective optimization, the optimal power outputs of 16.993 W & 19.20837 W and efficiencies of 1.9583 % & 2.25932 % are obtained on the Pareto front for models with and without considering the Thomson effect respectively. These optimal points are determined using the Shannon entropy decision-making approach which is found to be the best technique.

Simulation of a multi-floor evacuation with the extended social force model in a multi-corridor environment

Abstract For the stagnation problem that easily occurs in the process of simulating multi-floor and multi-staircase evacuation using a traditional social force model, in this paper, it is proposed to improve the social force model by introducing a moth-flame optimization algorithm, thus to establish a new evacuation model. The model firstly integrates the field model into a social force model as the pedestrian self-driving direction. Meanwhile, an objective optimization function of minimum system evacuation time is established based on the evaluation indexes, such as staircase congestion degree and average velocity, and the moth-flame optimization algorithm is improved by introducing dynamic inertia weight and random reverse learning strategy, thus establishing an evacuation optimization method. Finally, a simulation and numerical analysis is carried out for the multi-floor evacuation process using the experimental simulation platform built, which deeply analyzes the key factors influencing the model, gives the change relationships among the parameters such as evacuation time, initial pedestrian velocity, the number of pedestrians and staircase width, and verifies the effectiveness of the model.

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

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

Comparative Analysis of MFO, GWO and GSO for Classification of Covid-19 Chest X-Ray Images

Medical images play a crucial role in the classification of various diseases and conditions. One of the imaging modalities is X-rays which provide valuable visual information that helps in the identification and characterization of various medical conditions. Chest radiograph (CXR) images have long been used to examine and monitor numerous lung disorders, such as tuberculosis, pneumonia, atelectasis, and hernia. COVID-19 detection can be accomplished using CXR images as well. COVID-19, a virus that causes infections in the lungs and the airways of the upper respiratory tract, was first discovered in 2019 in Wuhan Province, China, and has since been thought to cause substantial airway damage, badly impacting the lungs of affected persons. The virus was swiftly gone viral around the world and a lot of fatalities and cases growing were recorded on a daily basis. CXR can be used to monitor the effects of COVID-19 on lung tissue. This study examines a comparison analysis of k-nearest neighbors (KNN), Extreme Gradient Boosting (XGboost), and Support-Vector Machine (SVM) are some classification approaches for feature selection in this domain using The Moth-Flame Optimization algorithm (MFO), The Grey Wolf Optimizer algorithm (GWO), and The Glowworm Swarm Optimization algorithm (GSO). For this study, researchers employed a data set consisting of two sets as follows: 9,544 2D X-ray images, which were classified into two sets utilizing validated tests: 5,500 images of healthy lungs and 4,044 images of lungs with COVID-19. The second set includes 800 images, 400 of healthy lungs and 400 of lungs affected with COVID-19. Each image has been resized to 200x200 pixels. Precision, recall, and the F1-score were among the quantitative evaluation criteria used in this study.

Performance Analysis of Marine-Predator-Algorithm-Based Optimum PI Controller with Unified Power Flow Controller for Loss Reduction in Wind–Solar Integrated System

Transmission line losses are a crucial and essential issue in stable power system operation. Numerous methodologies and techniques prevail for minimizing losses. Subsequently, Flexible Alternating Current Transmission Systems (FACTSs) efficiently reduce transmission losses, and the Unified Power Flow Controller (UPFC) is a reactive power compensation controller. The parameter strength of the proportional–integral (PI) controller was calibrated with the Marine Predator Algorithm (MPA), a recent metaheuristic algorithm. An MPA-based optimum PI controller with a UPFC evaluates the optimal location of the UPFC and PI controller parameters to accomplish the desired research objective. The power rating of the UPFC was determined depending on the voltage collapse rating and power loss and an evaluated performance analysis of the MPA–PI-controlled UPFC on a modified IEEE-30 bus transmission network in MATLAB Simulink code. The Newton–Raphson method was used to perform the load flow analysis. Hence, the proposed MPA–PI controller was examined in contrast to preferred heuristic algorithms, the Artificial Bee Colony (ABC) and Moth Flame Optimization algorithms (MFO); the results showed that the MPA–PI controller exhibited better performance with an improved voltage profile and surpasses active power losses with the optimal placement of the UPFC device under different loading conditions. The active power loss, considering a UPFC with the proposed algorithm, reduced from 0.0622 p.u to 0.0301 p.u; consequently, the voltage profile was improved in the respective buses, and the loss percentage reduction during a 100% base load was 68.39%, which was comparatively better than the ABC and MFO algorithms.

Low-rank and periodic group sparse based signal denoising method for rolling bearing fault feature extraction

Vibration signal analysis is a vital method of achieving rolling bearing fault feature extraction, but the fault impulses contained in the vibration signals are susceptible to interference from noise, making it difficult to extract fault features. In order to effectively extract the fault features of rolling bearings, an adaptive low-rank (LR) and periodic group sparse (AdaLRPGS) denoising method is proposed. Firstly, an AdaLRPGS model is constructed, which is not only an improvement of the classical sparse LR method, but also can effectively enhance the LR and periodic group sparsity of the failure impulses. Secondly, an adaptive period prior matching method is proposed to adaptively match the period of the fault impulses, and the moth flame optimization algorithm is utilized to adaptively search the regularization parameter of the AdaLRPGS model, and then the rule for determining the regularization parameter is summarized in the simulation analysis, which solves the dependence of the AdaLRPGS model on the period prior and regularization parameter. Finally, the solution procedure of the AdaLRPGS model is derived under the framework of the alternating direction method of multipliers. The simulated and measured signals are analyzed using the proposed method and compared with some advanced methods. The results show that the proposed method can extract the fault features of rolling bearings and has significant advantages compared with some advanced methods.

Voting Classifier for The Interactive Design with Deep Learning for Scene Theory

Tool products play a pivotal role in assisting individuals in various domains, ranging from professional work to everyday tasks. The success of these tools is not solely determined by their functionality but also by the quality of user experience they offer. Designing tool products that effectively engage users, enhance their productivity, and provide a seamless interaction experience has become a critical focus for researchers and practitioners in the field of interaction design. Scene theory proposes that individuals perceive and interpret their surroundings as dynamic "scenes," wherein environmental and situational factors influence their cognitive processes and behavior. This research paper presented a novel approach to the interaction design of tool products by integrating scene theory, flow experience, the Moth Flame optimization (MFO), cooperative game theory (CGT), and voting deep learning. Tool products play a vital role in various domains, and their interaction design significantly influences user satisfaction and task performance. Building upon the principles of scene theory and flow experience, this study proposes an innovative framework that considers the contextual factors and aims to create a seamless and enjoyable user experience. The MFO algorithm, inspired by the behavior of moth flame, is employed to optimize the design parameters and enhance the efficiency of the interaction design process. Furthermore, CGT is integrated to model cooperative relationships between users and tool products, fostering collaborative and engaging experiences. Voting deep learning is employed to analyze user feedback and preferences, enabling personalized and adaptive design recommendations. With the proposed CGT, this paper investigates the impact of the proposed approach on user engagement, task efficiency, and overall satisfaction. The findings contribute to the field of interaction design by providing practical insights for creating tool products that align with users' cognitive processes, environmental constraints, flow-inducing experiences, and cooperative dynamics.

Optimization of Abrasive Water Jet Machining Process Parameters on Onyx Composite Followed by Additive Manufacturing

Fiber-reinforced additive manufacturing components have been used in various industrial applications in recent years, including in the production of aerospace, automobile, and biomedical components. Compared to conventional methods, additive manufacturing (AM) methods can be used to obtainin lighter parts with superior mechanical properties with lower setup costs and the ability to design more complex parts. Additionally, the fabrication of onyx composites using the conventional method can result in delamination, which is a significant issue during composite machining. To address these shortcomings, the fabrication of onyx composites via additive manufacturing with the Mark forged 3D-composite printer was considered. Machinability tests were conducted using abrasive water jet machining (AWJM) with various drilling diameters, traverse speeds, and abrasive mass flow rates. These parameters were optimized using Taguchi analysis and then validated using the Genetic algorithm (GA) and the Moth Flame Optimization algorithm (MFO). The surface morphology (Dmax) and the roughness of the drilled holes were determined using a vision measuring machine with 2D software (MITUTOYO v5.0) and a contact-type surface roughness tester. Confirmation testing demonstrated that the predicted values werenearly identical to the experimental standards. During the drilling of an onyx polymer composite, regression models, genetic algorithms and the Moth-Flame Optimization algorithm were used to estimate the response surface of delamination damage and surface roughness.

Load frequency control with moth-flame optimizer algorithm tuned 2-DOF-PID controller of the interconnected unequal three area power system with and without non-linearity

Load frequency control with moth-flame optimizer algorithm tuned 2-DOF-PID controller of the interconnected unequal three area power system with and without non-linearity

The Optimal Design of a Hybrid Solar PV/Wind/Hydrogen/Lithium Battery for the Replacement of a Heavy Fuel Oil Thermal Power Plant

Renewable energies are clean alternatives to the highly polluting fossil fuels that are still used in the power generation sector. The goal of this research was to look into replacing a Heavy Fuel Oil (HFO) thermal power plant in Limbe, southwest Cameroon, with a hybrid photovoltaic (PV) and wind power plant combined with a storage system. Lithium batteries and hydrogen associated with fuel cells make up this storage system. The total cost (TC) of the project over its lifetime was minimized in order to achieve the optimal sizing of the hybrid power plant components. To ensure the reliability of the new hybrid power plant, a criterion measuring the loss of power supply probability (LPSP) was implemented as a constraint. Moth Flame Optimization (MFO), Improved Grey Wolf Optimizer (I-GWO), Multi-Verse Optimizer (MVO), and African Vulture Optimization Algorithm (AVOA) were used to solve this single-objective optimization problem. The optimization techniques entailed the development of mathematical models of the components, with hourly weather data for the selected site and the output of the replaced thermal power plant serving as input data. All four algorithms produced acceptable and reasonably comparable results. However, in terms of proportion, the total cost obtained with the MFO algorithm was 0.32%, 0.40%, and 0.63% lower than the total costs obtained with the I-GWO, MVO, and AVOA algorithms, respectively. Finally, the effect of the type of storage coupled to the PV and wind systems on the overall project cost was assessed. The MFO meta-heuristic was used to compare the results for the PV–Wind–Hydrogen–Lithium Battery, PV–Wind–Hydrogen, and PV–Wind–Lithium Battery scenarios. The scenario of the PV–Wind–Hydrogen–Lithium Battery had the lowest total cost. This scenario’s total cost was 2.40% and 18% lower than the PV–Wind–Hydrogen and PV–Wind–Lithium Battery scenarios.

Stochastic optimization for energy economics and renewable sources management: A case study of solar energy in digital twin

As power system might be damaged and even shut down due to the high uncertainty effects, they are always challenged by the high load variations. This study proposes a novel technique for managing power in microgrids (MGs) that have stochastic loads in the presence of high penetration of renewable energy sources. It is likely that the security of the users will be violated as a result of centralized optimization techniques that require considerable computing power. Conversely, in distributed methods, each load and generation are presumed to be linked to a single bus, thereby neglecting the electrical grid and its limitations. Due to this, the suggested distributed energy management strategy (EMS) decomposes the optimization problem into two levels, involving the MG centralized controller, local controllers, distribution network, and related restrictions. Because it is impossible to predict the loads precisely, the loads are regarded as stochastic variables which are modeled using probabilistic approach. The moth flame optimization algorithm is used to solve the optimization problem. A distributed EMS has been developed that integrates solar/photovoltaic (PV) units, wind turbines, diesel generators, and batteries in a digital twin area. Based on the simulations, the suggested distributed EMS is shown to be efficient and useful for the practical studies.

Weighted Moth-Flame Optimization Algorithm for Edible Oil Quality Detection Using Microwave Technologies

Weighted Moth-Flame Optimization Algorithm for Edible Oil Quality Detection Using Microwave Technologies

DPGWO Based Feature Selection Machine Learning Model for Prediction of Crack Dimensions in Steam Generator Tubes

The selection of an appropriate number of features and their combinations will play a major role in improving the learning accuracy, computation cost, and understanding of machine learning models. In this present work, 22 gray-level co-occurrence matrix features extracted from magnetic flux leakage images captured in steam generator tubes’ cracks are considered for developing a machine learning model to predict and analyze crack dimensions in terms of their length, depth, and width. The performance of the models is examined by considering R2 and RMSE values calculated using both training and testing data sets. The F Score and Mutual Information Score methods have been applied to prioritize the features. To analyze the effect of different machine learning models, their number of features, and their selection methods, a Taguchi experimental design has been implemented and an analysis of variance test has been conducted. The dynamic population gray wolf algorithm (DPGWO) has been adopted to select the best features and their combinations. Due to the two contradictory natures of performance metrics, Pareto optimal solutions are considered, and the best one is obtained using Deng’s method. The effectiveness of DPGWO is proved by comparing its performance with Grey Wolf Optimization and Moth Flame Optimization algorithms using the Friedman test and performance indicators, namely inverted generational distance and spacing.

Fuzzy adaptive tuning control of power system based on moth-flame optimization algorithm

Since low-frequency oscillation seriously threatens the safe operation of the power system, the power system stabilizer (PSS) can effectively suppress the oscillation. In this paper, a hybrid parameter optimization method combining the moth-flame optimization (MFO) algorithm and fuzzy logic controller (FLC) is proposed to address the problem of poor adaptability of the parameter tuning method in the conventional power system stabilizer (CPSS). This method can optimize the parameters of PSS in different processes. Initially, the optimal parameters of PSS under the current perturbation are given by the MFO algorithm. During the online operation of the system, as perturbation changes, the parameters of the PSS will also be adaptively tuned by the FLC in real-time when the system operating conditions change. According to this method, a fuzzy adaptive proportional–integral–differential (FPID) controller is designed based on the moth-flame optimization algorithm (MFO-FPID), and it is used as PSS to improve dynamic stability performance during oscillation. Moreover, its parameters can be adaptively adjusted in different perturbation scenarios. The designed MFO-FPID controller is applied to the single machine infinite bus (SMIB) power system to compare the dynamic performance with other controllers, that is, proportional–integral–differential (PID) and CPSS. The result shows that the MFO-FPID controller can suppress the oscillation very well, and the control effect is the best.

A Novel Rolling Bearing Fault Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM

Rolling bearings are critical in maintaining smooth operation of rotating machinery and considerably influence its reliability. The signals collected from rolling bearings in field conditions are often subjected to noise, creating a challenge to extract weaker fault features. This paper proposes a rolling bearing fault diagnosis method that addresses the above-mentioned problem through the moth-flame optimization algorithm optimized variational mode decomposition (MFO-optimized VMD) and an ensemble differential evolution online sequential extreme learning machine (DE-OSELM). By using the dynamic adaptive weight factor and genetic algorithm cross operator, the optimization accuracy and global optimization ability of the moth-flame optimization (MFO) are improved, and the two basic parameters of VMD decomposition level and quadratic penalty factor are adaptive selected. Since the vibration characteristics of the signal cannot be fully interpreted by a single index, The effective weighted correlation sparsity index (EWCS) is utilized to extract the relevant intrinsic mode functions (IMF) of VMD decomposition and extract their energies as features. In order to improve the classification accuracy, The energy feature set is subsequently inputted into DE-OSELM for training and classification purposes, and the proposed method is assessed via a sample set with four different health states of actual rolling bearings. Our proposed method results are compared with other diagnosis methods, proving its feasibility to diagnose rolling bearing faults with higher classification accuracy.

Chaos Moth Flame Algorithm for Multi-Objective Dynamic Economic Dispatch Integrating with Plug-In Electric Vehicles

Dynamic economic dispatch (DED) plays an important role in the operation and control of power systems. The integration of DED with space and time makes it a complex and challenging problem in optimal decision making. By connecting plug-in electric vehicles (PEVs) to the grid (V2G), the fluctuations in the grid can be mitigated, and the benefits of balancing peaks and filling valleys can be realized. However, the complexity of DED has increased with the emergence of the penetration of plug-in electric vehicles. This paper proposes a model that takes into account the day-ahead, hourly-based scheduling of power systems and the impact of PEVs. To solve the model, an improved chaos moth flame optimization algorithm (CMFO) is introduced. This algorithm has a faster convergence rate and better global optimization capabilities due to the incorporation of chaotic mapping. The feasibility of the proposed CMFO is validated through numerical experiments on benchmark functions and various generation units of different sizes. The results demonstrate the superiority of CMFO compared with other commonly used swarm intelligence algorithms.