Implementation and comparative analysis of evolutionary algorithms for energy optimization in wireless sensor networks

Wireless sensor networks will be at the epitome of applications in near future. It is going through tremendous positive changes. Although it suffers from some limitations and it sorts out its limitations day by day. The biggest limitation of wireless sensor networks is the limited energy of the nodes. Most of the energy is used during the routing of the data. An optimized way of routing will save the valuable energy of the node and helpful for increasing the network's lifetime. The optimization algorithms are indeed utterly helpful in many applications across the different fields to optimize the resources. In the proposed worked we have applied Adaptive Particle Swarm Optimization (APSO), Ant Colony Optimization (ACO), Genetic Algorithms (GA), and Simulated Annealing (SA) to a Modified Rendezvous Point Selection Scheme and observed the effect on the network lifetime and energy consumption in the network. We have made an exhaustive comparison of all the optimization algorithms with considerable simulations.