Weighted Salp Swarm Algorithm and its applications towards optimal sensor deployment

Abstract

Recent trends indicate the rapid growth of nature-inspired techniques in the field of optimization. Salp Swarm Algorithm (SSA) is a recently introduced stochastic algorithm that is inspired by the navigational capability and foraging behavior of Salps. However, classical SSA gives unsatisfactory results on higher dimension problems depicting poor convergence rate. The search process of SSA lacks exploration and exploitation resulting in convergence inefficiency. This paper proposes a strategy based on the weighted distance position update called Weighted Salp Swarm Algorithm (WSSA) to enhance the performance and convergence rate of the SSA. The proposed WSSA is validated using different benchmark functions and analyzed against seven different stochastic algorithms. The validation results confirmed enhanced performance and convergence rate of WSSA. Moreover, the proposed variant is applied for optimal sensor deployment task. WSSA approach is applied on probabilistic sensor model to maximize coverage and radio energy model to minimize energy consumption. This strategy is a trade-off between coverage and energy efficiency of the sensor network. It was observed that WSSA algorithm outperformed all the other stochastic algorithms in optimizing coverage and energy efficiency of Wireless Sensor Network (WSN).