Browsing by Author "Rajapaksha, S"

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Open Access
Adaptive Path Planning for Mobile Robots Using a Hybrid PRM–GA Optimization Approach
(John Wiley and Sons Ltd, 2026-04-10) Jathunga, T; Rajapaksha, S; Jayasinghe, S; Abeygunawardena, N
This study addresses the challenge of path planning in mobile robots, that requires efficient navigation in complex environments. Traditional approaches often struggle to meet the increasing demands of modern multi-robot systems operating in dynamic environments. To address these limitations, this study proposes an improved path planning technique by combining the probabilistic roadmap (PRM) with the genetic algorithm (GA), forming a hybrid PRM–GA approach designed to optimize the routes of mobile robots. Experiments were carried out for scenarios involving 2, 3, and 9 robots to analyze the performance of the proposed method under increasing complexity. The proposed PRM–GA method was compared with widely used path planning algorithms including (Formula presented.), Rapidly exploring random tree (RRT), and conventional PRM. Performance of each method was evaluated focusing on path efficiency and energy consumption. The enhanced fitness function within the GA evaluates robot paths based not only on distance but also on smoothness and turn count, promoting routes with fewer directional changes. The proposed PRM–GA method reduces robot energy consumption while improving navigation efficiency. Experimental results demonstrate that the PRM–GA hybrid method outperforms (Formula presented.), RRT, and PRM by encouraging smoother paths with fewer turns, thereby enhancing the operational efficiency of multi-robot systems. The effectiveness of the proposed approach highlights its potential for practical applications in sectors where efficient mobile robot navigation is essential.genetic algorithm
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Adaptive Robotic Voice Modulation for ASD Kids: Tailored Voice Pitch, Tone, and Speed
(Institute of Electrical and Electronics Engineers Inc., 2025) Panduwawala, P; Pulasinghe, K; Rajapaksha, S
Children with Autism Spectrum Disorder (ASD) often experience sensory sensitivities, particularly auditory hypersensitivity, which can make interactions and communication challenging. This study explores the customization of the NAO robot's voice pitch, tone, and speech speed using the Kaldi Speech Recognition Toolkit to align with the preferences of children with ASD. Eight distinct voice profiles were created, offering a range of variations in pitch, tone, and speech speed. Parents or caretakers were asked to select the voice profile they felt would be most suitable for their child. Based on this feedback, we created a spectrum of voices tailored to each child's needs. Results indicate that medium-pitch and moderate-speed combinations are most effective in enhancing engagement, with Voice 2 emerging as the preferred profile. The findings underscore the potential of adaptive voice modulation in improving robotic interactions for ASD therapy and highlight opportunities for further research in real-time adaptability and long-term impact assessment.
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Adding Common Sense to Robots by Completing the Incomplete Natural Language Instructions
(IEEE, 2022-07-18) De Silva, G. W. M. H. P.; Rajapaksha, S; Jayawardena, C
This system is developed to identify and complete the human’s instructions or incomplete sentences given by a user as a command. It would facilitate the interaction between the human and mobile service robots. However, when humans give the instruction, there can be incompleteness or else missing the information related to the environment. That is because humans, generally based on common sense, depending on the environment. Then the human brain can complete all those incomplete sentences by using common sense knowledge. This paper itself introduced a model of a service robot who can compete with the given incomplete instructions, display the related sentences or words, and finally move to the related objects in the environment. First, it will consider and identify the objects in the environment and then consider the given natural language instruction by humans. As a first step of the approach, complete the incomplete sentences. Those sentences are coming as natural language instructions. By parsing it into as the frame can identify the related words by using the created model or can call as language model and here used some identify words from the human common sense also, then the service robot will learn about the commonsense knowledge automatically from the parsing sentences as a speaker. Considering all the parsing sentences, it calculates and measures the accuracy of this service robot model. Simply this is a commonsense reasoning model. The result of the provided solution can enable the robot model that works in a ROS environment to identify and automatically perform the tasks.
Open Access
Adding Common Sense to Robots Using Ontology
(International Association of Computer Science and Information Technology, 2025-04-11) Ranathunga, R.A.A.L; Rajapaksha, S
This work investigates how ontological frameworks might improve robots’ ability to reason using common sense. The goal of the project was to enhance robot decision-making in dynamic real-world situations by developing an ontology-based model retraining technique. The researchers wanted to incorporate organized commonsense knowledge into robotic systems, so they built extensive ontologies that captured knowledge about the physical world and human interactions. The research compared the performance of robots with conventional models (control group) to those with ontology-enhanced models (experimental group) across various measures. The results indicate that this strategy may be used to develop more competent and user-friendly robotic helpers for a variety of sectors, including industry, healthcare, and education. Although the study has limitations related to data quality and experimental design, it does demonstrate the promise of ontology-based techniques to advance autonomous systems and human-robot interactions. Extending ontology databases, multidisciplinary cooperation, and investigating applications in other sectors are some of the future research goals.
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AI-Driven Integrated Caregiving and Health Monitoring Framework for Elderly Well-Being
(Institute of Electrical and Electronics Engineers, 2026-07-22) Nugaliyadde, S; Nikeshi, N; Marasinghe, M; Rajapaksha, C; Rajapaksha, S; Thelijjagoda, S
The rapid growth of the aging population has brought about some serious challenges, particularly in managing illnesses, feelings of loneliness, cognitive decline, and mental health issues. Traditional caregiving methods often depend on occasional assessments and hands-on supervision, which can fall short in providing the ongoing and adaptable support that’s really needed. This paper introduces an innovative caregiving and monitoring framework powered by AI, aimed at offering integrated, real-time, and comprehensive assistance for older adults. The system harnesses the power of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and health data analytics to combine physical health monitoring, nutrition planning, smart routine coaching, and therapist-led mental health support all in one platform. With features like voice-based conversations and journaling, it makes emotional expression and behavioral analysis more accessible, helping to gain a deeper insight into users’ mental well-being. Predictive analytics and anomaly detection are used to spot early signs of health risks and shifts in behavior, allowing for timely interventions. Plus, remote access means caregivers and healthcare professionals can keep an eye on users and offer informed advice. By shifting caregiving from a reactive approach to a proactive and preventive one, this system not only improves quality of life but also encourages independent living and eases the burden on caregivers.
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AI-Driven Prediction of Optimal Player Positions Based on Fundamental Skills in Netball
(Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Karunanayaka S.K.N.C; Wijayasiri B.M.G; Weerathunga W.A.R.N; Hemashi T.G.B; Wimalaratne, G; Rajapaksha, S
This research presents an innovative, video-based performance monitoring system designed to assess and enhance the fundamental skills of netball players while predicting optimal playing positions. Using advanced computer vision and machine learning techniques, the system evaluates fundamental skills through automated video analysis. The framework integrates Convolutional Neural Networks (CNNs), with feature extraction powered by pre-trained ResNet50 models, and temporal sequence analysis using Long-Short-Term Memory (LSTM) networks. In addition, random forest classifiers, supported by SMOTE-balanced data sets, are used for accurate position prediction. The system also incorporates centroid-based motion tracking through OpenCV, enabling precise monitoring of player movements. Unlike traditional subjective coaching methods, data-driven insights offer tailored training recommendations. The predictive analytics component also anticipates player development trajectories and role assignment. By transforming conventional netball training into an AI-driven predictive process, this research provides a powerful tool for coaches and players, improving skill evaluation accuracy, optimizing player positioning, and fostering informed evidence-based decision-making. Ultimately, this innovation contributes to improved individual performance, more cohesive team dynamics, and increased competitive success.
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Autonomous Hydroponic Environment with Live Remote Consulting System for Strawberry Farming
(IEEE, 2022-12-09) Samaranayake, S.L; Krishmal, S; Cooray, P.L.R.K; Senatilaka, T; Rajapaksha, S; Nuwanthika, W.S
Strawberries are a very popular fruit and are widely consumed all over the world. Due to its nutritional value, its consumption has increased tremendously in recent times. Strawberry, which has such high health and economic value, is grown in only one area in Sri Lanka. This is since the climate in those areas is favorable for strawberries. Using the Internet of Things, image processing, and machine learning, this research proposed a design for a closed environment with automatic monitoring and controlling of environmental factors and nutrition required for strawberry cultivation with the capability of remote live monitoring and analysis of each plant. Also, the proposed system captures the images of each strawberry plant using a camera navigation system and analyses those images using a machine learning algorithm to identify the growing stage. This decision making process was verified using strawberry pictures acquired from a strawberry farm. In addition, current capturing images can use in the next growth cycle to increase accuracy. The proposed system can be easily expanded by increasing the height of the tower and refrigeration power. Through this, strawberry cultivation can be expanded to all parts of Sri Lanka by overcoming climatic and geographical limitations.
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Behavior & Bio metric based Masquerade Detection Mobile Application
(Springer, Cham, 2019-07-29) Chandrasekara, P; Abeywardana, H; Rajapaksha, S; Sanjeevan, p
Mobile phone has become an important asset when it comes to information security since it has become a virtual safe. However, to protect the information inside the mobile, the manufacturers use the technologies as password protection, face recognition or fingerprint protection. Nevertheless, it is clear that these security methods can be bypassed. That is when the urge of a post-authentication is coming to the surface. In order to protect the phone from an unauthorized or illegitimate user this method is proposed as a solution. The aim of the proposed solution is to detect the illegitimate user by monitoring the behavior of the user by four main parameters. They are: 1) Keystroke dynamics with a customized keyboard; 2) location detection; 3) voice recognition; 4) Application usage. In the initial state machine learning is used to train this mobile application with the authentic user’s behavior and they are stored in a central database. After the initial training period the application is monitoring the usage and comparing it with the already saved data of the user. Another unique feature of this is the prevention mechanism it executes when an illegitimate user is detected. Furthermore, this application is proposed as an inbuilt application in order to avoid the deletion of app or uninstallation of the app by the intruder. With this Application which is introduced as “AuthDNA” will help you to protect the sensitive information of your mobile device in a case of theft and bypassing of initial authentication.
Open Access
Between Absence and Dreams: A Phenomenological Study on the Experiences of Malaiyaha Tamil Children with Migrant Mothers
(Faculty of Humanities and Sciences, SLIIT, 2025-05-27) Wathna, S; Mahaliyanage, E; Senarath, K; Rajapaksha, S; Sethini, R; Kodikara, V; Subasinghe, V; Selvaratnam, N.D; Ponnamperuma, L
Maternal labor migration has become a significant socioeconomic strategy among structurally marginalized communities in Sri Lanka, particularly within the Malaiyaha Tamil estate sector. While existing research has largely focused on economic and educational outcomes, there remains limited understanding of children’s lived experiences within these contexts. This study aimed to explore how Malaiyaha Tamil children make sense of their mothers’ migration for overseas employment. A qualitative design informed by Interpretative Phenomenological Analysis (IPA) was employed. Semi-structured interviews were conducted with adolescents aged 12-17 years residing in plantation communities. Data were analyzed ideographically before identifying shared experiential patterns across cases. The analysis generated three interrelated superordinate themes: (1) mother as emotional anchor and “absent presence,” (2) childhood reassigned through role reorganization and educational vulnerability, and (3)living between longing and aspiration within structural constraint. Findings indicate that maternalmigration is experienced as a relational paradox, where financial provision coexists with emotionaldisruption. Children described ambivalent attachment experiences, shifting family roles, and variededucational engagement shaped by available support systems. Despite these challenges, participantsarticulated future aspirations that reflected resilience within structurally constrained environments.Drawing on Attachment Theory, Ecological Systems Theory, and Ambiguous Loss Theory, the studyconceptualizes maternal migration as a relational and ecological process rather than a singular economicevent. The findings highlight the coexistence of vulnerability and agency and underscore the importanceof context-sensitive interventions for children in migration-affected communities.
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Cloud-based Salesman-Bot for Ontology-based Negotiation
(IEEE, 2023-04-06) Fernando, A; Rahubedda, T; Jayasinghe, B; Mallikahewa, S; Hettiarachchi, O; Rajapaksha, S
We have proposed a cloud-based ChatBot (Salesman-Bot) approach to handling multiple negotiation scenarios in a supermarket environment. The web application is a simple interface that can be implemented on a single standalone device or interacted with through a mobile phone. The Salesman-Bot responds both via text and speech. By introducing a Salesman-Bot, efficient negotiation, with quick preferences and suggestions can be provided. A new architecture proposed to operate the Salesman-Bot together with Google APIs and libraries such as Natural Language AI, Vision AI, Speech to Text API, Text to Speech API and Machine Learning using TensorFlow. The application also uses the Google Cloud Platform with related services such as Google App Engine. The goal is to make ChatBots more efficient in negotiating in different business scenarios. This paper presents the work carried out with ontology and machine learning in a cloud-based environment to handle multiple negotiation scenarios based on a negotiation hierarchy. It also proposes the opportunities and drawbacks of such a system.
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Computer Vision Based Navigation Robot
(IEEE, 2022-12-26) Haputhanthri, M; Himasha, C; Balasooriya, H; Herath, M; Rajapaksha, S; Harshanath, S.M.B.
The majority of industrial environments and homewares need help when exploring unknown locations owing to a lack of understanding about the building structure and the various impediments that may be faced while transporting products from one spot to another. This is because there is a lack of knowledge about the building structure and the potential obstacles that may be encountered. This paper provides “Computer Vision-Based Navigation Robot” as a strategy for indoor navigation with optimal accessibility, usability, and security, decreasing issues that the user may encounter when traveling through indoor and outdoor areas with real-time monitoring of the most up to date IoT technology. The article is titled “Indoor Navigation with Optimal Accessibility, Usability, and Security.” This article proposes “Computer Vision-Based Navigation Robot” as a solution for interior navigation that provides optimum accessibility, usability, and security. This is done in order to tackle the issue that was presented before. Since the readers of this post include people who work in industry as well as physically challenged people who live alone, CVBN Robot takes object-based inputs from its surroundings. This is because the audience for this essay includes both groups of people. This study also covers a variety of methods for localization, sensors for the detection of obstacles, and a protocol for an Internet of Things connection between the server and the robot. This connection enables real-time position and status updates for the robot as it navigates a known but unknown interior environment. In addition, this study covers a variety of methods for localization, sensors for the detection of obstacles, and a protocol for an Internet of Things connection between the server and the robot.
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Development of Agent-Based Centralized Tool For Analyzing and Managing Security-Enhanced Linux Policies using WebSocket Protocol
(IEEE, 2022-02-23) Rajapaksha, S; Kularatna, I. M
The SELinux facilitates and includes an extensible "MAC" structure built within the Linux kernel. An application or a process life cycle that runs as a user has the specific authority to access objects such as files, sockets, and other processes with Linux’s default Discretionary Access Control (DAC). SELinux prescribes the access and the progress privileges of each user, application, process, and the files on the system and administers the communications of these elements utilizing a security strategy that determines how severe or indulgent a given Red Hat Enterprise Linux establishment ought to be. However, its constraints such as, not being user-friendly, having too complicated policies, and complex policy description language, are limiting the implementation of SELinux policies in the Information Technology industry. As a result, there is only a little research available on User Interface-based policy management tools. Even those researches have limitations such as the inability to remotely manage a host/server, manual documentation, and the inability to monitor the systems automatically from a dashboard. In order to overcome said research gap and problems, this research will implement a system using a web-socket technology that facilitates the ability to converse in full-duplex through just one TCP connection. This system is included with a web socket-agent, which can be installed in server endpoints and can change SELinux policies, a web-socket server: which can do live communication with the agent to perform policy changes along with the UI component: to manage policies using the user interface and a database component to store policy details.
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Development of Agent-Based Centralized Tool For Analyzing and Managing Security-Enhanced Linux Policies using WebSocket Protocol
(IEEE, 2022-04-14) Kularatna, I. M; Rajapaksha, S
The SELinux facilitates and includes an extensible "MAC" structure built within the Linux kernel. An application or a process life cycle that runs as a user has the specific authority to access objects such as files, sockets, and other processes with Linux’s default Discretionary Access Control (DAC). SELinux prescribes the access and the progress privileges of each user, application, process, and the files on the system and administers the communications of these elements utilizing a security strategy that determines how severe or indulgent a given Red Hat Enterprise Linux establishment ought to be. However, its constraints such as, not being user-friendly, having too complicated policies, and complex policy description language, are limiting the implementation of SELinux policies in the Information Technology industry. As a result, there is only a little research available on User Interface-based policy management tools. Even those researches have limitations such as the inability to remotely manage a host/server, manual documentation, and the inability to monitor the systems automatically from a dashboard. In order to overcome said research gap and problems, this research will implement a system using a web-socket technology that facilitates the ability to converse in full-duplex through just one TCP connection. This system is included with a web socket-agent, which can be installed in server endpoints and can change SELinux policies, a web-socket server: which can do live communication with the agent to perform policy changes along with the UI component: to manage policies using the user interface and a database component to store policy details.
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Elderly Care Home Robot using Emotion Recognition, Voice Recognition and Medicine Scheduling
(IEEE, 2022-12-26) Kularatne, B.M.U.S.; Basnayake, B.M.J.N.; Sathmini, P.D.L.A.M.; Sewwandi, G.V.U; Rajapaksha, S; De Silva, D
The robotic concept is used for several tasks to easier human day-to-day tasks. There are various recreational studies have been done on the elderly people’s care system. In this study, the system can identify the elderly people’s emotional status using thermal image processing that eliminates the halo effect issue in thermal images using a single discriminator Cycle-GAN model, serving medicine to elderly people by moving towards the elderly person while avoiding obstacles using point to point algorithm and obstacle avoidance and identify the semantic analysis by using web ontology based language. The integrated system is evaluated using the Gazebo simulator because the cost is lower than implementing the features in a real robot.
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Farming Through Technology Driven Solutions For Agriculture Industry Ceylon E-Agro mobile application-find technology based solutions for agricultural problems
(Institute of Electrical and Electronics Engineers, 2022-09-16) Imalka, L.A.; Gunawardana, K.G.A.; Kodithuwakku, K.M.S.K; Arachchi, H.K.E; Harshanath, S.M.B; Rajapaksha, S
Many developing countries are based on the agricultural sector. More than 60 percent of the population depends on this sector. This project is focused on maize cultivation. In agriculture, farmers play the most important role. Currently, farmers are facing many problems related to maize cultivation in Sri Lanka. This mobile application will help the maize farmers to overcome these difficulties and provide a good consumer demand for maize cultivation. Through this mobile application, the farmer can find solutions for pest & diseases in maize, fire threat in the farm field. AI based Agri Agent will be provide real-time solutions, bring the farmers and the buyers into the one platform, and provide price prophesying, price index feature. IoT based smart farming features will be provided to remain soil moisture and quality of soil for maize plantation.
Open Access
Identification of Some Major Research Gaps in Lean Manufacturing and Industry 4.0 Integration
(SLIIT Business School, 2023-12-14) Rajapaksha, S; Jayarathne, P.G.S.A; Galdolage, S
In the contemporary business world, digitalization has become a significant factor in economic development. Lean is a prominent topic in both academia and business. Due to the evolution of digital technologies that promote Industry 4.0 because of the industrial revolution, the integration of Lean Manufacturing and Industry 4.0 has attracted the attention of academics and managers. This conceptual paper discusses the integration of Lean Manufacturing and Industry 4.0, as well as the research gaps and prospective research opportunities related to integration. Systematic Literature Review (SLR) in the PRISMA framework has been used to identify research gaps, and future studies could be planned to close these gaps to benefit academia and industry stakeholders. Keyword co-occurrence analysis has been performed using VOSviewer software to identify areas that need the attention of researchers. Articles were extracted from the Scopus database using the keywords "Lean Manufacturing" and "Industry 4.0". This study concentrates on identifying research gaps that industry leaders and managers would be interested in, rather than the gaps in conceptual nature.
Open Access
Improved Path Planning for Multi-Robot Systems Using a Hybrid Probabilistic Roadmap and Genetic Algorithm Approach
(Department of Agribusiness, Universitas Muhammadiyah Yogyakarta, 2025-03-24) Jathunga, T; Rajapaksha, S
This study focuses on the development and application of an improved Probabilistic Roadmap (PRM) algorithm enhanced with Genetic Algorithms (GA) for multi-robot path planning in dynamic environments. Traditional PRM-based methods often struggle with optimizing path length and minimizing turns, particularly in complex, multi-agent scenarios. To address these limitations, we propose a hybrid PRM-GA approach that incorporates genetic operators to evolve optimal paths for multiple robots in real-time.The research contribution is an enhanced PRM-GA framework that improves efficiency in multi-robot navigation by integrating evolutionary techniques for dynamic obstacle handling and optimized path generation.The research methodology involves testing the algorithm in various environments, including varying robot numbers and environmental complexities, to evaluate its scalability and effectiveness. Our results demonstrate that the PRM-GA algorithm successfully reduces both path lengths and turn counts compared to standard PRM-based methods, ensuring collision-free and smooth paths. The algorithm showed robust performance across different scenarios, effectively handling dynamic obstacles and multi-agent coordination. However, in highly dynamic environments with rapidly changing obstacles and constraints, the algorithm may occasionally produce paths with turn counts and distances similar to or slightly higher than those of simpler approaches due to the need for frequent re-optimization. Future research can explore incorporating additional factors such as energy consumption and time optimization, alongside distance and turns, to further enhance the algorithm's efficiency in real-world applications. Overall, the PRM-GA approach advances the state of the art by offering a more adaptable and scalable solution for multi-robot path planning, with applications in logistics, industrial automation, and autonomous robotics.
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An Improved Round Robin Algorithm for an Efficient CPU Scheduling
(IEEE, 2022-02-23) Bandara, A. M. O; Rajapaksha, S
The algorithm policy applied by a CPU, to schedule running processes, has an impact on the efficiency of an operating system (OS). As a result, a superior CPU scheduling algorithm leads to higher OS performance while using limited resources and for shorter periods of time. As a result, several strategies have been suggested and implemented to improve CPU scheduling performance. Any scheduler’s primary responsibility is to assign jobs to the most effective and reliable resource available. It’s also a fact that if jobs and resources aren’t planned properly, the entire operating system’s productivity suffers significantly. The "Round Robin" is deemed an effective and fair approach because of each process is allocated the same amount of time quantum. Nevertheless, the effectiveness of the system is determined by the selected time-quantum. The major goal of this research is to develop the present round-robin algorithm by enhancing the time quantum for candidate processes in real-time in such a way that its impartiality is maintained. There is no loss of property. This paper’s proposed algorithm finds the time left in a process’s last turn, and then Determines whether it is time-based on a certain threshold value, should quantum be expanded or not. An arithmetic simulation has been created to demonstrate that the suggested method is correct. It outperforms the traditional round-robin algorithm. In words of several performing metrics such as average waiting time, context switches and the number of turnaround times, the suggested adjusted version of the round- robin algorithm outperforms the traditional round-robin algorithm, according to the results of the experimental investigation.
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Intelligent Adaptive Lighting Control: Reinforcement Learning-Based Optimization for Smart Home Energy Efficiency
(Institute of Electrical and Electronics Engineers Inc., 2025) Hewakapuge M.M; Gamage W.G.T; Surendra D.M.B.G.D; Thejan K.G.T; Rajapaksha, S; Rajendran, K
This study introduces a novel research paper outlining a behavioral-based adaptive lighting system that aims to revolutionise smart home lighting by integrating user behavior tracking to enhance energy efficiency and user comfort. Unlike traditional motion-sensor-based lighting, the novelty of this approach is the ability to adapt dynamically to evolving user behaviors through reinforcement learning. The system utilises Wi-Fi-based positioning, GPS and accelerometer data to monitor user movements and classify different areas of the house. Users initially calibrate the home layout through a mobile application, marking room locations and lighting configurations. The system then collects movement data over time to predict optimal lighting schedules based on user routines and refines the predictions and updates lighting adjustments accordingly, minimising energy wastage while maximising user convenience. A serverless backend architecture ensures scalability, cost-effectiveness, and seamless data processing. The adaptive framework continuously refines lighting automation, responding to evolving behavioral patterns.
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Intelligent System to Verify the Effectiveness of Proposed Teacher Transfers Incorporating Human Factors
(IEEE, 2022-02-23) Karunanayake, V; Wanniarachchi, J. C; Karunanayake, p; Rajapaksha, S
A data center architecture can be determined as the basic structure of a cloud computing data center. However, the expected outcome cannot be obtained even though the tools, technologies, and elements are advanced. It is crucial to select the right architecture as architecture used, which directly affects the operational cost and power consumption of the data center. The SDN, Software Defined Networking, is a novel technology of cloud computing. This SDN is used to separate the control panel and forwarding plane as well, as it enables programmable network components. Therefore, the components of the SDN can easily control and managed. This research aimed to select an SDN-based architecture for a data center. Here, there are two parameters of average throughput and average packet delay on the three existing architectures that are tested using simulation. Then the results are compared with each other. In this research, the accuracy correlations among average packet delay and average throughput are investigated. The Obtained results show that Fat-Tree architecture is the best one among VL2 and hybrid architecture consists with flat tree and VL2 technology in terms of their performances.