Optimization Algorithm Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Optimization Algorithm projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Optimization Algorithm Final Year Projects

A Multilingual Spam Reviews Detection Based on Pre-Trained Word Embedding and Weighted Swarm Support Vector Machines

This project focuses on detecting fake or misleading online reviews that can mislead customers and harm businesses. It uses a machine learning model called Weighted Support Vector Machine combined with an optimization algorithm called Harris Hawks Optimization to improve accuracy. The method works for multiple languages, including English, Spanish, and Arabic. The system was tested with different techniques and datasets, achieving high accuracy in identifying spam reviews, especially during the COVID-19 pandemic when online reviews increased dramatically.
Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images

This project develops a smart system to detect diabetic eye disease early using Internet-connected devices and deep learning. Eye images are collected with IoT devices and sent to the cloud for processing. The system cleans the images, highlights damaged regions, extracts important features, and uses an AI model to classify the disease. Tests show this method is more accurate and effective than earlier approaches.
Enhancing Breast Cancer Classification in Histopathological Images through Federated Learning Framework

This study develops an automated system to detect breast cancer from medical images. It secures patient data by encrypting images and storing them safely using advanced machine learning techniques. The system then classifies the images using a deep learning model, which is optimized for better accuracy. Tests show the method is highly effective, achieving over 95% accuracy and reliability.
HDLNET A Hybrid Deep Learning Network Model With Intelligent IOT for Detection and Classification of Chronic Kidney Disease

This project focuses on detecting similarities in software code when the original source is not available. It uses neural machine translation to analyze small code sections called basic blocks across different computer architectures. The method extracts features faster and more accurately than previous approaches. Tests show it achieves 92% accuracy and can work up to 16 times faster using GPUs.
Neural-Hill A Novel Algorithm for Efficient Scheduling IoT-Cloud Resource to Maintain Scalability

This project focuses on making smart devices work more efficiently with cloud servers. It introduces a new method called Neural-Hill, which combines AI and optimization techniques to manage cloud resources for Internet of Things (IoT) devices. The system helps process tasks faster, reduces delays, and handles more devices without slowing down. Experiments show it improves service quality and scales well as more devices connect.
Quantum Artificial Hummingbird Algorithm for Feature Selection of Social IoT

This project creates a smart method to pick only the most useful information from large Internet of Things data. It uses an improved search technique inspired by hummingbird behavior and quantum ideas. The method helps computers work faster and make better decisions with fewer inputs. Tests on many datasets show that it improves accuracy while reducing unnecessary data.
TMaLB A Tolerable Many-Objective Load Balancing Technique for IoT Workflows Allocation

This project studies how to balance heavy and uneven data loads in Internet of Things systems. It looks at what factors matter most for good service, such as cost, speed, and energy use. The method uses an intelligent search algorithm to choose the best way to share work across devices. It improves both performance and the number of tasks the system can handle.
Gauss-Mapping Black Widow Optimization With Deep Extreme Learning Machine for Android Malware Classification Model

This project focuses on improving the detection of Android malware, which is increasingly common and hard to identify using traditional methods. It uses a combination of advanced machine learning and deep learning techniques to automatically classify malware. The model selects the most important features and optimizes its parameters to achieve high accuracy. Tests show that this approach can detect malware more effectively than existing methods, reaching almost 99% accuracy.
Henry Gas Solubility Optimization With Deep Learning Based Facial Emotion Recognition for Human Computer Interface

This project focuses on automatically recognizing human emotions from facial expressions. It uses deep learning to analyze faces and detect different emotions. The system removes noise from images, extracts important features, and trains a model to classify emotions accurately. Tests show it works very well, reaching about 99% accuracy.
A Hybrid Discrete Artificial Bee Colony Algorithm for Imaging Satellite Mission Planning

This project focuses on planning imaging satellite missions efficiently. The researchers developed a new algorithm called HDABC, which improves how satellites are scheduled to capture images. The algorithm uses smart strategies to explore possible solutions, improve the best ones, and help weaker solutions learn from stronger ones. Experiments show that this method works well for different mission scenarios.
A Recent Review on Approaches to Design Power System Stabilizers Status Challenges and Future Scope

This project reviews methods for improving the stability of modern power systems. Power networks are complex and can face sudden failures due to rotor-angle instability. Power system stabilizers (PSS) help reduce oscillations and maintain stability. The study highlights current design methods, challenges, and opportunities for making PSS more efficient and reliable.
A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

This paper explains how smart group-based algorithms can help build and train artificial neural networks more effectively. These algorithms search for better network designs and improve how the network learns. The authors review many types of these algorithms and show how they are used to improve different neural networks. It gives readers a clear picture of current methods and their applications.
Applicable Image Security Based on Computational Genetic Approach and Self-Adaptive Substitution

This project focuses on keeping digital images safe during storage and transmission. It introduces a new method that scrambles and secures images using advanced computational and chaotic techniques. The approach was tested on standard image datasets and showed strong resistance against hacking attempts. Overall, it provides a reliable solution for protecting multimedia data in modern communication systems.
Improved Sparrow Search Algorithm Optimized DV-Hop for Wireless Sensor Network Coverage

This project improves how wireless sensor networks find the positions of nodes. It uses a new algorithm called GSSADV-Hop, which reduces location errors by adjusting node hop calculations and using a smart search strategy. The method is faster and more accurate than traditional techniques, achieving very low positioning errors. It helps make sensor networks more reliable and efficient for practical use.
Location Optimization Based on Improved 3D DV-HOP Algorithm in Wireless Sensor Networks

This project improves how wireless sensor networks locate their nodes in complex 3D environments. It modifies an existing algorithm to reduce errors and increase accuracy. The method uses multiple communication ranges and a particle swarm optimization technique. Experiments show it works fast and precisely, achieving about 96% accuracy.
Logistics UAV Air Route Network Capacity Evaluation Method Based on Traffic Flow Allocation

The project creates a model to check how many delivery drones an air route network can safely handle. It studies how traffic, safety gaps, and efficiency affect drone movement. The model is solved using an improved search algorithm to find the best capacity. Tests show that the method gives reliable results and can be applied to real-world drone delivery routes.
Optimal Wireless Sensor Networks Allocation for Wooded Areas Using Quantum-Behaved Swarm Optimization Algorithms

This project develops an algorithm to place wireless sensors efficiently in forest areas. It reduces the number of sensors needed while keeping them connected. The method uses advanced swarm-based optimization techniques inspired by quantum behaviors. It checks distances, line of sight, and avoids obstacles to ensure reliable sensor coverage.
Using Neural Network and Levenberg-Marquardt Algorithm for Link Adaptation Strategy in Vehicular Ad Hoc Network

This project focuses on improving communication in Vehicular Ad Hoc Networks (VANETs), which help reduce accidents by sharing road information between vehicles. It uses a neural network to adjust data transmission based on the quality of the communication channel and the movement of vehicles. The method considers changes in speed and direction to send messages more reliably. Simulations show that this approach is much faster and more efficient than existing methods.
Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images

This project develops a smart system to detect diabetic retinopathy, a disease that can cause blindness in diabetic patients. It uses IoT devices to collect eye images and sends them to the cloud for analysis. The system cleans and enhances the images, identifies damaged regions, and uses advanced deep learning methods to accurately diagnose the disease. This approach aims to help doctors detect the condition early and improve healthcare outcomes.

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Optimization Algorithm Final Year Projects with Source Code