Optimization Projects for ME, MTech, Masters, MS abroad, and PhD electrical engineering students. These Optimization IEEE projects are implemented with future work and extension for final year students with research paper writing and publishing. These EEE research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Optimization Projects
-
A Survey on Reconfigurable Intelligent Surface for Physical Layer ecurity of Next-Generation Wireless Communications
This project studies new ways to make future 6G wireless networks faster and more secure. It focuses on using special smart surfaces that can control signals to prevent eavesdropping. The research reviews different methods to improve security for various network types. It also discusses challenges and ideas for future wireless systems. -
ACERAC: Efficient Reinforcement Learning in Fine Time Discretization
This project focuses on teaching machines to learn the best actions on their own instead of being told what to do. It improves how machines try different actions over time so they learn smoothly without sudden jerks. The researchers created a new learning method that remembers past actions and uses them to make better decisions. Tests in simulation showed this method works better than other popular approaches in most cases. -
Combining Lyapunov Optimization and Deep Reinforcement Learning for D2D Assisted Heterogeneous Collaborative Edge Caching
This project focuses on improving content sharing in wireless networks. Devices can share data directly with nearby devices or get it from a base station. The method uses smart learning to decide which devices should store and share content. It reduces delays, saves energy, and keeps the network running smoothly. -
Deep Tensor Spectral Clustering Network via Ensemble of Multiple Affinity Tensors
This project focuses on grouping data more accurately using a method called tensor spectral clustering. The researchers created a new network, TSC-Net, that learns the data patterns in one step. It reduces memory use by only looking at small parts of the data at a time. Tests show that it groups data better than older methods. -
Hierarchical Reinforcement Learning for Multi-Layer Multi-Service NonTerrestrial Vehicular Edge Computing
This project focuses on improving computing for smart vehicles. It combines ground-based and satellite edge computing to help vehicles process data faster and in more areas. The system uses machine learning to decide where and how much data to process for lower delay and energy use. Simulations show that this approach works better than existing methods. -
Learning End-to-End Hybrid Precoding for Multi-User mmWave Mobile System With GNNs
This project focuses on improving wireless communication in millimeter wave systems. It uses a smart learning method to design the transmitter signals directly from received signals. This approach works well even when users move or the network changes. The method reduces errors, speeds up communication, and works for different network setups without extra training. -
Multi-Agent Double Deep Q-Learning for Fairness in Multiple-Access Underlay Cognitive Radio Networks
This project focuses on improving how wireless devices share the same communication channel without causing interference. It uses artificial intelligence to make sure all devices get a fair chance to send data. The system learns automatically to balance speed and fairness. This makes communication faster, more reliable, and efficient even when many users share the same signal space. -
Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies
This project focuses on making wireless networks smarter and more secure. It uses artificial intelligence to manage network resources for many users and base stations. The system also studies how a jammer can disrupt the network and how to defend against it. The methods are tested in simulations to show they work well. -
BucketAugment Reinforced Domain Generalisation in Abdominal CT Segmentation
This project focuses on improving how computers identify organs in CT scans. The researchers created a new method called BucketAugment. It helps deep learning models work well on medical images from different hospitals and machines. This method makes organ segmentation more accurate without major changes to existing systems. -
Data-Driven Gradient Regularization for Quasi-Newton Optimization in Iterative Grating Interferometry CT Reconstruction
This project focuses on improving breast cancer imaging using a new CT technique called GI-CT. The researchers developed a smart algorithm named GradReg that makes CT images clearer and less noisy. It works well for both conventional and GI-CT scans and can help reduce the radiation dose. Overall, it makes it easier to detect details in breast images. -
A Multi-Layer Information Dissemination Model and Interference Optimization Strategy for Communication Networks in Disaster Areas
This project studies how information spreads in communication networks used during disasters. It creates a model to understand how messages travel between nodes and how network interference affects this process. The work also finds the best network setup to reduce interference and deployment cost. Simulations show the model accurately predicts information flow and helps improve disaster communication. -
ChannelComp: A General Method for Computation by Communications
This project develops a new method called ChannelComp that allows multiple wireless devices to combine their signals digitally at a receiver. Unlike traditional approaches, it works with digital communication, which is more reliable and widely used. The method finds the best way to encode signals for computing functions over the air. Simulations show it performs much better than older techniques, especially for multiplying signals. -
High-Speed Resource Allocation Algorithm Using a Coherent Ising Machine for NOMA Systems
This project focuses on improving wireless communication using a technique called NOMA, which allows more data to be sent at once. The main challenge is deciding how to share channels and power efficiently. The researchers use a new method called a coherent Ising machine to quickly find good solutions. Their method is faster and more effective than existing approaches. -
Joint RIS-Aided Precoding and Multislot Scheduling for Maximum User Admission in Smart Cities
This project focuses on improving wireless networks in smart cities using special surfaces called reconfigurable intelligent surfaces (RIS). These surfaces can control how signals travel, helping more users get better service. The study develops a method to schedule users and adjust signals efficiently, even when the system is complex. Tests show this approach serves more users and works well even when network information is slightly outdated. -
Meta Federated Reinforcement Learning for Distributed Resource Allocation
This project focuses on improving how cellular networks share resources like power and channels. Instead of relying on a central server, users handle much of the computation locally. The method helps save energy, reduce network traffic, and adapt quickly to changing conditions. It also allows users to collaborate, making the system faster and more efficient than traditional approaches. -
Optimal RIS Partitioning and Power Control for Bidirectional NOMA Networks
This project studies how special smart surfaces, called RIS, can improve wireless communication in NOMA networks. The method divides the surface to make signals stronger for users and removes the need for adjusting uplink power. It tests different scenarios to ensure good quality and fair data rates. Simulations show that using RIS greatly improves network performance. -
Optimizing Step-Size of Perturb & Observe and Incremental Conductance MPPT Techniques Using PSO for Grid-Tied PV System
This project is about getting the most power from solar panels. It uses a smart method that combines two techniques to adjust the solar system quickly when sunlight changes. The new method works faster and gives more efficiency than older methods. It helps solar panels produce nearly their maximum power under different weather conditions. -
Energy Management System for Hybrid Renewable Energy-Based Electric Vehicle Charging Station
This project is about making electric vehicle charging cheaper and greener. It uses solar and biogas energy to power the charging station. A smart system decides the best time and amount of power to charge vehicles. This approach saves money and reduces pollution while being profitable for the station owner.
Did you like this research project?
To get this research project Guidelines, Training and Code…
How We Help You with Optimization Projects
At UniPhD, we provide complete guidance and support for Optimization ieee projects for BE, BTech, MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.
Our experts guide students all over India, including in Mumbai, Delhi, Bangalore, Hyderabad, Ahmedabad, Chennai, Kolkata, Pune, Jaipur, and Surat. We also assist students in the USA, UK, Canada, Australia, Singapore, Malaysia, and Thailand. They have extensive experience in computer science, electronics, electrical and all engineering domains.
Optimization Thesis and Dissertation Writing
UniPhD has a team of experienced academic writers who specialize in Optimization research and thesis development. We offer fast-track dissertation writing services to help you complete your Optimization thesis or dissertation smoothly and on time.
Our M.E., M.Tech, Masters, MS abroad, and PhD theses are developed according to individual university guidelines and checked with plagiarism detection tools to ensure originality and quality.
Optimization Research Paper Publishing Support
UniPhD provides complete support for research paper writing, editing, and proofreading to help you publish your work in reputed journals or conferences. We accept documents in Microsoft Word, RTF, or LaTeX formats and ensure your paper meets publication standards.
Project Synopsis and Presentation Support
We help you prepare your project synopsis, including the problem definition, objectives, and motivation for your dissertation. Our team also provides complete PPT, documentation, and tutorials to make your final presentation successful. You can also download complete project resources, including source code, a project report, a PPT, a tutorial, documentation, and a research paper for your Optimization final year project.
Optimization Research Support for PhD Scholars
UniPhD offers advanced Optimization research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.
Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.