Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm

Abstract

In complex two-dimensional monitoring environments, how to enhance network efficiency and network lifespan while utilizing limited energy resources, and ensuring that wireless sensor networks achieve the required partial coverage of the monitoring area, are the challenges of optimizing coverage in wireless sensor networks.With the premise of ensuring connectivity in the target network area, an enhanced multi-objective salp swarm algorithm based on non-dominated sorting (EMSSA) is proposed in this paper, by jointly optimizing network coverage, node utilization, and network energy balance objectives. Firstly, the logistic chaotic mapping is used to maintain the diversity of the initial salp swarm population. Secondly, to balance global and local search capabilities, a new dynamic convergence factor is introduced. Finally, to escape local optima more effectively, a follower updating strategy is implemented to reduce the blind following of followers while retaining superior individual information. The effectiveness of the strategy is validated through comparative experiments on ZDT and DTLZ test functions, and the proposed algorithm is applied to coverage optimization in WSNs in complex environments. The results demonstrate that the algorithm can adjust coverage thresholds according to different application requirements, providing various effective coverage optimization configurations. With the same preset requirements for partial coverage achieved, both network efficiency and lifespan have been significantly improved.

Multi-Strategy Improved Particle Swarm Optimization Algorithm and Gazelle Optimization Algorithm and Application

“…Chaotic motion is non-repetitive and has characteristics of randomness and ergodicity. In recent years, chaotic mapping has been used by many scholars [45][46][47][48][49] for optimization algorithms, and has achieved good results in improving population diversity. The initial population of the GOA when solving optimization problems is randomly generated data.…”