Optimal routing using whale optimization and lion optimization algorithm in WSN

In wireless sensor networks, cluster-based routing plays a key role in data transmission which increases the network lifetime and scalability of networks. Many researchers provide a quality contribution in this area. Still, there are many challenges related to residual energy, stability of routes, network lifetime, and local optima. According to the latest research Meta heuristic algorithms give better results as compared to the classical model for selecting optimal routes. In this work, a hybrid Meta heuristic approach is present that combines the features of whale optimization and lion optimization (WL-optimization). In the proposed approach multi-objective function is used for selecting the optimal CH and routes. This proposed WL-optimization replaces the exploitation phase of Whale optimization with a mutated Lion operator to prevent earlier trapping of solutions into local optima. It prevents delayed convergence and maintains the harmonious balance between exploitation and exploration which will reflect in the stability of routes. The selection of optimal routes depends upon parameters like energy, distance with SN and BS, traffic rate, delay, throughput, PDR, PTR, and quality of service. The proposed model compares with many metaheuristic algorithms and it improves the performance in the above parameters.