Optimized lowest ID in wireless sensor network using Invasive Weed Optimization (IWO)-genetic algorithm (GA)

Wireless Sensor Network (WSN) is an emerging application that has proved to be very effective due to its wide application and so has become very prominent various industries and research WSN's life is improved through clustering-based routing. Operation and network life are controlled by a large deployed sensor network whose major characteristic is self-organization and energy efficiency. The area of challenge is energy efficiency as it is limited, valuable and is hard to find. The lifetime of sensor network is extended through many clustering protocols that reduce utilization of power. Clustering operations are optimized through swarm optimizations and evolutionary algorithms. Invasive Weed Optimization (IWO) is a numerical protocol which is continuous and stochastic and provides a simple evolutionary mechanism with clarity for optimization. Limitation of local optimality is overcome by Tabu Search (TS) which makes use of linear programming algorithms and specialized heuristics. In this study, hybrid optimization technique is used to address local minima problem.