Heuristic Algorithms Final Year Projects with Source Code

Byadmin Final Year Projects

Heuristic Algorithms Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Heuristic Algorithms 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.

Heuristic Algorithms Final Year Projects

  1. Distributed Split Computing System in Cooperative Internet of Things IoT
    This project focuses on improving how IoT devices work together to process data. Instead of sending all tasks to the cloud, nearby IoT devices share the computing work. The system decides which devices should help based on energy use and speed. This approach reduces energy consumption by over 20% while ensuring tasks finish on time.
  2. A New Unsupervised Validation Index Model Suitable for Energy-Efficient Clustering Techniques in VANET
    This project focuses on improving how clusters in data are evaluated. It introduces a new method called M2I that measures both how similar nodes are within a cluster and how separate clusters are from each other. This method works better than existing techniques, especially in dynamic networks like VANETs. Tests show that M2I gives very accurate results in both simulated and real-world data.
  3. Autonomous Decentralized Spectral Clustering for Hierarchical Routing of Multi-Hop Wireless Networks
    This project focuses on organizing multi-hop wireless networks into clusters automatically, without a central controller. It develops a method that allows devices to form groups using only local information, even when the network changes or nodes move. The approach uses mathematical equations to create a structure that guides clustering. It works for both fixed and dynamic networks, making network management easier and more scalable.
  4. 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.
  5. Reinforcement Learning for Delay Tolerance and Energy Saving in Mobile Wireless Sensor Networks
    This project uses a type of machine learning called Q-learning to improve wireless sensor networks. It focuses on choosing special nodes, called cluster heads, to collect and send data efficiently. The method reduces energy use and shortens the travel path of a mobile base station. Simulations show it performs better than traditional approaches in saving energy and extending network life.
  6. Vehicle Path Planning Based on Pedestrian Collision Avoidance Action
    This project focuses on making autonomous vehicles safer around pedestrians. It studies how pedestrians move to avoid collisions and predicts their intentions using a deep neural network and Hidden Markov Model. Based on this prediction, the vehicle plans a path to avoid hitting them. Simulations show that this method predicts pedestrian actions faster and successfully avoids collisions.
  7. WSN Clustering Routing Algorithm Combining Sine Cosine Algorithm and Lvy Mutation
    This project focuses on improving wireless sensor networks by making them last longer and use energy more evenly. It introduces a smart method to choose leader nodes based on their energy and position. The method also uses advanced search techniques to optimize node selection. Overall, it helps the network work efficiently and balance the workload among all nodes.
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