Optimization PSO Research Articles

An Efficient Task Scheduling in a Cloud Computing Environment using Hybrid Swarm Optimization

Information technology (IT) services are available on demand over the Internet under the current computing standard known as cloud computing. A work is mapped with an available resource in the cloud environment to get a good result. Cloud computing has emerged as a transformative technology that provides on-demand access to a shared pool of computing resources over the internet. Effective task scheduling plays a crucial role in optimizing resource utilization and improving overall system performance in cloud environments. According to a server's workload capacity, tasks are distributed across its virtual machines (VMs) via task scheduling. The server's workload is planned out to reduce traffic and wait times. The best algorithm currently in use to schedule a work to an existing resource in a cloud setting is particle swarm optimization (PSO). PSO schedules the task for an available resource to cut down on computational costs. In order to address job scheduling challenges in the cloud environment, a hybrid swarm optimizations (HSO) algorithm—a combination of PSO and salp swarm optimization (SSO)—is suggested in this paper. The hybrid algorithm leverages the strengths of both PSO and SSO to achieve better optimization results. The proposed method is evaluated using the CloudSim simulator and compared with traditional scheduling algorithms. Simulation results demonstrate the superiority of the hybrid PSO-SSO algorithm in terms of makespan time, cloud throughput, and task execution efficiency. The findings suggest that the hybrid approach can significantly enhance resource utilization and performance in cloud computing environments.

Feature Selection From MagFace Face Recognition Model With Optimization Algorithms

In recent years, many studies have been carried out in the field of artificial intelligence in the literature with the development of equipment. Face recognition algorithms have an important place among these developments. Among the face recognition algorithms, the most successful ones are usually deep learning approaches. Models such as SphereFace, CosFace, ArcFace, and MagFace are important deep learning models in the literature. Despite their success, deep learning models are often computationally costly. Therefore, advanced methods are needed to reduce the computational load for these models. One of the most valid methods for this is to choose the most valuable one among embedding features for face recognition. Thus, cost can be reduced, and accuracy values can be increased even more. In this study, the most valuable of the 512 embedded features in the MagFace model was tried to be obtained by using PSO, GA, SCA, and DE optimization algorithms. As a result, accuracy values of 99.83%, 98.57%, and 98.65% were reached for 193, 252, and 280 features selected in the LFW, CFP, and AGEDB datasets, respectively.

A Hybrid Grey Wolf Optimization and Constriction Factor based PSO Algorithm for Workflow Scheduling in Cloud

Due to its flexibility, scalability, and cost-effectiveness of cloud computing, it has emerged as a popular platform for hosting various applications. However, optimizing workflow scheduling in the cloud is still a challenging problem because of the dynamic nature of cloud resources and the diversity of user requirements. In this context, Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms have been proposed as effective techniques for improving workflow scheduling in cloud environments. The primary objective of this work is to propose a workflow scheduling algorithm that optimizes the makespan, service cost, and load balance in the cloud. The proposed HGWOCPSO hybrid algorithm employs GWO and Constriction factor based PSO (CPSO) for the workflow optimization. The algorithm is simulated on Workflowsim, where a set of scientific workflows with varying task sizes and inter-task communication requirements are executed on a cloud platform. The simulation results show that the proposed algorithm outperforms existing algorithms in terms of makespan, service cost, and load balance. The employed GWO algorithm mitigates the problem of local optima that is inherent in PSO algorithm.

Mikrodizi Veri Kümesi Üzerinde Doğadan İlham Alan Optimizasyon ile Birleştirilen Uyarlanabilir Ağ Tabanlı Bulanık Çıkarım Sistemi Kullanılarak T-ALL, B-ALL ve T-LL Malignitelerinin Sınıflandırılması

Leukemia is the formation of cancer with different characteristic findings. According to the progress type of disease in the body is called acute or chronic. Acute leukemias are characterized by the presence of blast cells that proliferate uncontrollably in the bone marrow and then go into the blood and tissues. Determination of T/B or non T/B cell class is important in the immunophenotypic evaluation related to subtypes of blast cells. Because the diagnosis and treatment processes of B-ALL, T-ALL and T-LL subtypes, which are composed of B and T cell lines, are different. Therefore, correct diagnosis is vital. In this study, the molecular diagnosis was provided for the accurate detection of T-ALL, B-ALL and T-LL subtypes through microarray datasets. But, microarray datasets have a multidimensional structure. Because it contains information related to the disease as well as information not related to the disease. This situation also affects the training situation and computational cost of the model. For this, the whale optimization algorithm was used in the first stage of the study. Thus, related genes were selected from the data set. Secondly, the selected potential genes were given as input to the ANFIS structure. Then, in order to improve the inference power, parameter optimization related to the membership function of the ANFIS structure was provided with ABC and PSO optimization algorithms. Finally, the predictions obtained from the ANFIS, ANFIS+ABC, and ANFIS+PSO methods for each sample were classified using the logistic regression algorithm and, an accuracy rate of 86.6% was obtained.

A Comparative Analysis of OLSR Routing Protocol based on PSO and Cuckoo Search Optimization (CSO) in Manets

New developments in wireless communication have enabled the use of highly efficient and inexpensive wireless receivers in a variety of portable applications. Each node in a mobile network is a mobile device that independently organizes its own connection to the others and manages its own data transmissions. The adaptability, scalability, and cost reduction of mobile networks have attracted considerable attention. Because mobile networks are constantly changing, problems with routing and power usage are common. High error rates, energy limitations, and inadequate bandwidth are just a few of the issues plaguing mobile ad hoc networks. The relevance of routing protocols in dynamic multi-hop networks like Mobile Ad hoc Networks (MANET) has drawn the attention of many scholars. In this paper, we focus on implementing an OLSR(Optimised Link State Routing) protocol and evaluates its performance using two optmisation algorithm: Particle Swarm Optimization(OLSR) and Cuckoo Search Optimization (CSO). The simulation result suggests that PSO is superior to both CSO and the conventional OLSR routing technique. We implemented using NS-2 simulator for simulation and NAM for network animation.

A study on self-shielding effect of CaCO3 in cable pyrolysis using gas product analysis and PSO optimization

This paper investigates the self-shielding mechanism of CaCO3 during the pyrolysis of PVC-CaCO3 based cable. Previous studies have focused on HCl adsorption by CaCO3, neglecting the self-shielding effect. In this paper, thermogravimetric-infrared analyzer, pyrolysis gas chromatography-mass spectrometer and particle swarm optimization (PSO) algorithm are used to investigate the self-shielding mechanism. The results show a three-stage pyrolysis process. In the first stage, CaCO3 weakens the autocatalytic decomposition of PVC by absorbing HCl in the solid phase. The 3rd Avrami Erofeev model was obtained by PSO, representing the production of CaCl2 crystals. The second stage demonstrates self-shielding due to a reduction in solid-phase porosity. CaCO3 encapsulation inhibits the pyrolysis of PVC resulting in macromolecular intermediates. The 3rd order-based model was obtained by PSO, with a lower pyrolysis reaction rate compared to pure PVC. In the third stage, the stability of Cl in the solid phase is weakened at high temperatures, which converts the Cl to the gas phase in the form of chlorinated hydrocarbons, leading to the failure of self-shielding effect of CaCO3. The 2nd order-based model was obtained by PSO, which is considered as a result of the solid phase reactions instead of pyrolysis.

An Efficient Combination of Genetic Algorithm and Particle Swarm Optimization for Scheduling Data-Intensive Tasks in Heterogeneous Cloud Computing

Task scheduling is still an open issue for improving the performance of cloud services. Focusing on addressing the issue, we first formulate the task-scheduling problem of heterogeneous cloud computing into a binary non-linear programming. There are two optimization objectives including the number of accepted tasks and the overall resource utilizations. To solve the problem in polynomial time complexity, we provide a hybrid heuristic algorithm by combing both benefits of genetic algorithm (GA) and particle swarm optimization (PSO), named PGSAO. Specifically, PGSAO integrates the evolution strategy of GA into PSO to overcome the shortcoming of easily trapping into local optimization of PSO, and applies the self-cognition and social cognition of PSO to ensure the exploitation power. Extensive simulated experiments are conducted for evaluating the performance of PGSAO, and the results show that PGSAO has 23.0–33.2% more accepted tasks and 27.9–43.7% higher resource utilization than eight other meta-heuristic and hybrid heuristic algorithms, on average.

Optimal Energy Consumption Path Planning for Unmanned Aerial Vehicles Based on Improved Particle Swarm Optimization

In order to enhance the energy efficiency of unmanned aerial vehicles (UAVs) during flight operations in mountainous terrain, this research paper proposes an improved particle swarm optimization (PSO) algorithm-based optimal energy path planning method, which effectively reduces the non-essential energy consumption of UAV during the flight operations through a reasonable path planning method. First, this research designs a 3D path planning method based on the PSO optimization algorithm with the goal of achieving optimal energy consumption during UAV flight operations. Then, to overcome the limitations of the classical PSO algorithm, such as poor global search capability and susceptibility to local optimality, a parameter adaptive method based on deep deterministic policy gradient (DDPG) is introduced. This parameter adaptive method dynamically adjusts the main parameters of the PSO algorithm by monitoring the state of the particle swarm solution set. Finally, the improved PSO algorithm based on parameter adaptive improvement is applied to path planning in mountainous terrain environments, and an optimal energy-consuming path-planning algorithm for UAVs based on the improved PSO algorithm is proposed. Simulation results show that the path-planning algorithm proposed in this research effectively reduces non-essential energy consumption during UAV flight operations, especially in more complex terrain scenarios.

Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques.

Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this article focuses on hyperparameter optimization for well-known machine learning models. It explores cutting-edge optimization methods such as metaheuristic algorithms, deep learning-based optimization, Bayesian optimization, and quantum optimization, and our paper focused mainly on metaheuristic and Bayesian optimization techniques and provides guidance on applying them to different machine learning algorithms. The article also presents real-world applications of hyperparameter optimization by conducting tests on spatial data collections for landslide susceptibility mapping. Based on the experiment's results, both Bayesian optimization and metaheuristic algorithms showed promising performance compared to baseline algorithms. For instance, the metaheuristic algorithm boosted the random forest model's overall accuracy by 5% and 3%, respectively, from baseline optimization methods GS and RS, and by 4% and 2% from baseline optimization methods GA and PSO. Additionally, for models like KNN and SVM, Bayesian methods with Gaussian processes had good results. When compared to the baseline algorithms RS and GS, the accuracy of the KNN model was enhanced by BO-TPE by 1% and 11%, respectively, and by BO-GP by 2% and 12%, respectively. For SVM, BO-TPE outperformed GS and RS by 6% in terms of performance, while BO-GP improved results by 5%. The paper thoroughly discusses the reasons behind the efficiency of these algorithms. By successfully identifying appropriate hyperparameter configurations, this research paper aims to assist researchers, spatial data analysts, and industrial users in developing machine learning models more effectively. The findings and insights provided in this paper can contribute to enhancing the performance and applicability of machine learning algorithms in various domains.

Simulation-based Multi-unit Collaborative Optimization in Data Centers

With the rapid development of information technology in China, the problem of energy conservation in data centers has become increasingly prominent, and the green data center model is inevitable. The refrigeration equipment which is the main power consumption in the data center is affected by the environment, room temperature, unit output, and other factors. This paper provides a scheme of multi-unit collaborative optimization cooling. It is mainly divided into four stages: first, a model of the data center is built through Modelica, and it is exported as a fmu file. Second, the PSO optimization strategy is used to generate the data sets which contain unit output and cooling effect at different times. Third, LSTM and TCN load forecasting models are trained. Finally, through a comparison of various optimization algorithms, the PSO-LSTM-TCN model is used to simulate the unit output and optimize the output parameters of multiple units.

Random Forest Optimization Using Particle Swarm Optimization for Diabetes Classification

Diabetes mellitus is a chronic degenerative disease caused by a lack of insulin production in the pancreas or the body's ability to use insulin less effectively. According to a report by the World Health Organization (WHO), 4% of the total deaths in the world are caused by diabetes. The International Diabetes Federation (IDF) notes that in 2013 there has been an increase in diabetes sufferers. Indonesia is the seventh place with the largest number of cases of diabetes mellitus. In this study, the method used to classify diabetes is using a random forest algorithm with Particle Swarm Optimization (PSO) optimization. This study resulted in an accuracy of the random forest classification algorithm of 78.2% and 82.1 using PSO optimization with an increase in value of 3.9%. It can be concluded that PSO optimization can provide a better increase in classification accuracy values when compared to the random forest algorithm without PSO optimization

A Novel Hybrid Algorithm Based on Jellyfish Search and Particle Swarm Optimization

Metaheuristic optimization is considered one of the most efficient and powerful techniques of recent decades as it can deal effectively with complex optimization problems. The performance of the optimization technique relies on two main components: exploration and exploitation. Unfortunately, the performance is limited by a weakness in one of the components. This study aims to tackle the issue with the exploration of the existing jellyfish search optimizer (JSO) by introducing a hybrid jellyfish search and particle swarm optimization (HJSPSO). HJSPSO is mainly based on a JSO structure, but the following ocean current movement operator is replaced with PSO to benefit from its exploration capability. The search process alternates between PSO and JSO operators through a time control mechanism. Furthermore, nonlinear and time-varying inertia weight, cognitive, and social coefficients are added to the PSO and JSO operators to balance between exploration and exploitation. Sixty benchmark test functions, including 10 CEC-C06 2019 large-scale benchmark test functions with various dimensions, are used to showcase the optimization performance. Then, the traveling salesman problem (TSP) is used to validate the performance of HJSPSO for a nonconvex optimization problem. Results demonstrate that compared to existing JSO and PSO techniques, HJSPSO contributes in terms of exploration and exploitation improvements, where it outperforms other well-known metaheuristic optimization techniques that include a hybrid algorithm. In this case, HJSPSO secures the first rank in classical and large-scale benchmark test functions by achieving the highest hit rates of 64% and 30%, respectively. Moreover, HJSPSO demonstrates good applicability in solving an exemplar TSP after attaining the shortest distance with the lowest mean and best fitness at 37.87 and 36.12, respectively. Overall, HJSPSO shows superior performance in solving most benchmark test functions compared to other optimization techniques, including JSO and PSO. As a conclusion, HJSPSO is a robust technique that can be applied to solve most optimization problems with a promising solution.

Hybrid Optimization Based Hindi Document Summarization Using Deep Learning Technique

The proliferation of textual information today is a result of the internet's recent development, which is widely accessible to anybody, at any time. Generally speaking, several Natural Language Processing (NLP) techniques can be used to analyze the textual information that is offered on the basis of text documents. In recent years, various text summarization techniques have been implemented in English text documents but a little amount of work is carried out in Hindi text documents summarization. In this research investigation, the Coot Remora Optimization (CRO) technique based on Deep Recurrent Neural Network (DRNN) is used to summarize Hindi documents. Here, the CRO algorithm is used to train the DRNN, which is used to compute the sentence scores.The highest scored sentences are going to included in the summary. When compared to recent optimization algorithmic techniques, such as MCRMR-SSO, Graph-based_PSO, Genetic Algorithms (GA), and Political Elephant Herding Optimization (PEHO) based Deep Long Short Term Memory (DLSTM) algorithm, the developed method is shown to be superior. Additionally, three evaluation metrics such as precision, recall, f-measure are used to analyze the performance of the CRO based DRNN technique and obtained high performance.

ANALISIS SENTIMEN REVIEW PADA APLIKASI MEDIA SOSIAL TIKTOK MENGGUNAKAN ALGORITMA K-NN DAN SVM BERBASIS PSO

Review sentiment analysis on social media applications is one of the methods used to analyze opinions and feelings (sentiment) of social media users towards a particular product, service or topic. Tiktok social media users are the second most in the world. The Tiktok app is the leading social media platform and the ultimate destination for short-form videos. Music, dance, education, beauty, passion, or talent show. This research uses data from Tiktok application reviews based on positive and negative sentiments to compare the K-Nearest Neighbor (K-NN) and Particle Swarm Optimization (PSO)-based Support Vector Machine (SVM) algorithms. To test the results of the PSO-based K-NN and SVM algorithms using the Cross Validation method from the test results that the PSO optimization SVM algorithm has the best accuracy compared to the KNN algorithm. Where the accuracy value of SVM is 86.40% and AUC is 0.908. The PSO optimization SVM has an accuracy of 88.20% and an AUC of 0.91. While the K-NN algorithm has an accuracy of 83.40% and an AUC of 0.903 then the accuracy value of the K-NN optimization PSO gets an accuracy of 69.20% and an AUC of 0.77. This means that the use of the PSO optimization SVM algorithm has the highest level of accuracy.

Thermal investigation and bi-objective optimization of a multi-product system with concentrated solar system

The current research is conducted with the aim of investigating a multi-generation energy system (MGS) for electricity, hydrogen, heating, cooling and hot water production using SRC, Kalina cycle and proton exchange membrane (PEM) electrolysis as the main subsystems. The thermodynamic analysis namely energy, exergy, and exergo-economic examination carried out. Analysis for each component is done after thermodynamic modeling. The exergy investigation showed that regenerator 2, vapor generator and condenser had the highest exergy destruction rate, respectively. A comparison was made between the current work and the basic system, and according to the results obtained, the system's ability to produce electricity, cooling, heating and hydrogen are calculated as 782 kW, 173.2 kW, 2265 kW and 0.01613 mol/day, respectively. Also, the energy and exergy efficiency of the proposed system increased by 3.13% and 5.37%, respectively. The optimization based on PSO method was carried out. The results of PSO optimization represented that in the case exergy destruction and exergy efficiency will be the optimization target these two outputs can reach the value of 2208 kW and 9.84%, while optimization based on exergy efficiency and production cost rate will lead to 9.86% and 166.6 $/h.

Capacitated Vehicle Routing Problem In Optimization Of Waste Truck Routes Using The Particle Swarm Optimization Algorithm

In this research process, the aim is to determine the shortest route for transporting waste with the shortest or minimum total distance. In this case, what must be considered is the transportation of waste, regarding the distance between the location of the waste source to the landfills. The problem of waste transportation routes can be made in a mathematical method. The method used in waste transportation is the Capacitated Vehicle Routing Problem (CVRP) method. In solving the problem of transporting waste using the CVRP method, distance must be minimized or optimized using the Particle Swarm Optimization Algorithm. On the initial route of work area 1 there is the o-p-q-r-s-o route which has a total distance of 133.5 Km, in which there are several villages such as the Mambang Muda TPA - Belungihit Village - Aek Hitetoras Village - Brussel Plantation Village - Aek Tapa Village - Mambang Muda TPA. And after the latest garbage transportation route uses the PSO algorithm, the optimal distance is 132.5 Km with the o-s-q-r-p-o route where the village is Mambang Muda TPA - Aek Tapa Village - Aek Hitetoras Village - Brussels Plantation Village - Belungihit Village - Mambang Muda Landfill. In WK 2 there is an initial route o-p-q-r-s-o which has a total distance of 153.9 Km, in which the route has several TPA Mambang Muda villages - Marbau Village - Lobu Rampah Village - Simpang 4 Village - Pernantian Plantation Village - Mambang Muda TPA. And after the latest garbage transportation route uses the PSO algorithm, the optimal distance and route is 127.7 Km. In work area 3 with the initial route and distance of 150 and after using the PSO algorithm, the optimal route and distance is 137.1 Km. In work area 4 with a route and distance of 127.8 Km, the optimal route and distance using the PSO algorithm is 149.8 Km. Work area 4 has a route and initial distance of 157.8 Km and the optimal PSO route and distance is 149.8. The 5th working area has an initial distance of 180.3 and the PSO route is 134.3 Km. The working area of ​​the 5 initial routes is 152.4 Km and the PSO route is 152.4.

OPTIMIZATION OF K-MEANS CLUSTERING USING PARTICLE SWARM OPTIMIZATION ALGORITHM FOR GROUPING TRAVELER REVIEWS DATA ON TRIPADVISOR SITES

K-Means Algorithm can be used to group tourists based on reviews on tourist destination objects. This algorithm has a weakness that is sensitive to the determination of the initial centroid. The initial centroid that is determined at random will decreasing the level accuracy, often gets stuck at the local optimum, and gets a random solution. Optimization algorithms such as PSO can overcome this by determining the optimal initial centroid. The optimal number of clusters (K) will be determined using the Elbow method by calculating the SSE value of the resulting cluster. The average Silhouette Coefficient (SC) is used to measure the quality of the clusters produced by the K-Means Algorithm with and without the PSO Algorithm. This study uses secondary data obtained from the UCI Machine Learning Repository with the name Travel Reviews Data Set which consists of 980 records and 10 attributes. The test results show that K=2 is the optimal number of clusters. The K-Means and PSO Algorithm gives an average SC value of 0.300358 which is better than without the PSO Algorithm of 0.300076. The optimal PSO hyperparameter generated is the number of particles=30, \varphi_1=2.2, and {\ \varphi}_2=3 at maximum iteration of 100.

Green hydrogen production ensemble forecasting based on hybrid dynamic optimization algorithm

Solar-powered water electrolysis can produce clean hydrogen for sustainable energy systems. Accurate solar energy generation forecasts are necessary for system operation and planning. Al-Biruni Earth Radius (BER) and Particle Swarm Optimization (PSO) are used in this paper to ensemble forecast solar hydrogen generation. The suggested method optimizes the dynamic hyperparameters of the deep learning model of recurrent neural network (RNN) using the BER metaheuristic search optimization algorithm and PSO algorithm. We used data from the HI-SEAS weather station in Hawaii for 4 months (September through December 2016). We will forecast the level of solar energy production next season in our simulations and compare our results to those of other forecasting approaches. Regarding accuracy, resilience, and computational economy, the results show that the BER-PSO-RNN algorithm has great potential as a useful tool for ensemble forecasting of solar hydrogen generation, which has important ramifications for the planning and execution of such systems. The accuracy of the proposed algorithm is confirmed by two statistical analysis tests, such as Wilcoxon’s rank-sum and one-way analysis of variance (ANOVA). With the use of the proposed BER-PSO-RNN algorithm that excels in processing and forecasting time-series data, we discovered that with the proposed algorithm, the Solar System could produce, on average, 0.622 kg/day of hydrogen during the season in comparison with other algorithms.

Classification of Blighted Ovum Factors in Pregnant Women Using PSO-Based Naïve Bayes

Classification of Blighted Ovum Factors or undeveloped fetuses is carried out considering that many cases occur in pregnant women. Blighted Ovum is 60% of the causes of miscarriage. In Indonesia, it is found in 37% of every 100 pregnancies. Classification uses Naïve Bayes based on Particle Swarm Optimization (PSO), which only requires small training data to determine the parameter estimates needed in the classification process, and the use of Particle Swarm Optimization can increase accuracy and solve optimization problems with the process of selecting variable data and attribute data to create a questionnaire as a data collection method. The results of the classification of blighted Ovum in pregnant women using the Naïve Bayes algorithm with the Rapid Miner framework obtained an accuracy value of 71.56% with an Area Under Curve (AUC) of 0.674 included in the excellent classification category. After using the PSO optimization, the accuracy value rose to 79.82% with an Area Under the Curve of 0.764, including a good classification category. Naïve Bayes is a suitable method for classification, and PSO can improve the accuracy and AUC values .

Optimizing Load Frequency Control in Standalone Marine Microgrids Using Meta-Heuristic Techniques

Integrating renewable resources into the electrical systems of marine vessels achieves the dual goal of diversifying energy resources and reducing greenhouse gas emissions. The presence of intermittent renewable sources and sudden nonlinear load changes can cause frequency deviations in isolated hybrid marine microgrids. To address this issue, the paper proposes a conventional PID (proportional–integral–derivative)-controller-based LFC (load frequency controller) which is optimized by meta-heuristic optimization algorithms, namely, PSO (particle swarm optimization), GWO (grey wolf optimization) and hybrid PSO-GWO. The proposed LFC was designed using transfer functions of various microgrid components, with ITAE (integral time absolute error) and ITSE (integral time square error) serving as performance indices. The proposed LFC’s validation was performed through HIL (hardware-in-loop) real-time simulation using a DS 1104 R&D controller board, with simulation results showing the better performance of the optimized frequency response compared to the nonoptimized LFC controller in terms of rise time, fall time, slew rate and overshoot. The hybrid PSO-GWO algorithm performs better than the other optimization algorithms. The simulation results demonstrate the stability and robustness of the proposed controller. In summary, the proposed PID-controller-based LFC can regulate frequency deviation in standalone hybrid marine microgrids effectively.