Particle Swarm Optimization Final Year Projects with Source Code

Byadmin Final Year Projects

Particle Swarm Optimization Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Particle Swarm Optimization 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.

Particle Swarm Optimization Final Year Projects

  1. A Multi-Stage Machine Learning and Fuzzy Approach to Cyber-Hate Detection
    This project focuses on detecting harmful content on social media. It uses machine learning methods to classify online messages as hateful or not. The study tests two models on multiple datasets and improves their accuracy using nature-inspired optimization techniques and fuzzy logic. This approach helps the system better understand the meaning behind the text.
  2. Modified Salp Swarm Algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images
    This project focuses on automatically detecting diseases in the gastrointestinal tract using images from a tiny camera capsule. The researchers developed a computer program that cleans the images, extracts important features, and classifies diseases like bleeding, ulcers, and polyps. They combined advanced deep learning techniques with optimization algorithms to improve accuracy. Tests on a medical image database showed the system can correctly identify diseases with over 98% accuracy.
  3. Anomaly-Based Intrusion on IoT Networks Using AIGAN-a Generative Adversarial Network
    This project studies how cyber attackers can trick smart security systems that protect computer networks. It focuses on poisoning attacks, which feed fake data to these systems to make them fail. The researchers use a type of deep learning called GAN to create realistic fake data and test how well security systems can detect it. Their experiments show that many machine learning models used in network security can be fooled by such attacks.
  4. 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.
  5. Automatic Generation Control Strategy Based on Deep Forest
    This project improves how electricity grids maintain stable power supply. It uses a smart system called a deep forest network to choose the best control method in real time. The system adjusts power output efficiently with fewer control actions. Simulations show it works better than traditional methods.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Optimized Range-Free Localization Scheme Using Autonomous Groups Particles Swarm Optimization for Anisotropic Wireless Sensor Networks
    This project improves how wireless sensor networks figure out the positions of nodes in irregular network areas. It enhances a common method called DV-Hop by reducing distance errors and using a smarter calculation technique. It also applies an optimization algorithm to make location estimates more accurate and stable. The study shows that these improvements work well even in challenging network conditions and suggests better ways to measure and improve localization in the future.
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