Optimising Energy Efficient Routing protocols in Wireless Sensor Networks - A comparative study

This study presents a comparative overview of optimization techniques that include, among others, Ant Colony Optimization, Firefly Algorithm, Bat Algorithm, Bee Colony Optimization, Q-learning, Dynamic Programming, and Branch and Bound Algorithm. Addressing each domain in detail, explanations will be offered for the methods described: for example, ACO’s foraging behavior, Firefly’s light attraction, Bat’s echolocation, and BCO’s collaborative approach. The Travelling Salesman Problem (TSP) is treated with the DP algorithm and Branch and Bound. Different tests in each algorithm are made, and their efficiency and weaknesses are approximated to guide the selection of a proper approach to be used in future optimizations. This research highlights the strengths and drawbacks of each method, thus enhancing the understanding of how these optimization techniques operate in various fields and contributing to the informed selection of algorithms for specific problems. The results provide practical insights and suggest directions for further improvement and application in real-world scenarios.