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Hybrid Optimization Method for WSN Data Collection using Mobile Sink based on TSP Approach

The path chosen by the mobile sink for data collection in a wireless sensor network must be the shortest possible path to reduce the data collection delay and data loss through buffer overflow. Finding the shortest path is a typical traveling salesman problem (TSP), which is a NP-hard combinatorial optimization problem. This work proposes the HWOAGWO algorithm which is a hybrid form of whale optimization algorithm (WOA) and the grey wolf optimization (GWO) algorithm to solve the TSP problem efficiently. Dimension learning based hunting strategy is used to avoid the algorithm getting trapped in local optima and slow convergence. Finally, simulation is used to prove that the proposed HWOAGWO algorithm is better at obtaining the best pathway over the other state-of-the-art algorithms. The algorithm is also tested in a real-time outdoor environment to prove its reliability.