An Asymmetric Dominated Multiobjective Optimization Algorithm for Reducing Energy Consumption of WSN Operation

Addressing the issue of inconsistent optimization speeds in coverage and sink path selection within wireless sensor network (WSN), this article introduces an asymmetric-dominance sorting genetic algorithm based on multiobjective optimization, termed ASGA. This novel algorithm employs a uniquely designed encoding pattern that optimizes both WSN coverage and sink mobility paths concurrently. In addition, due to the distinctive nature of the encoding, the algorithm’s crossover and mutation processes are categorically tailored, ensuring a more balanced search scope across both optimization targets. Furthermore, we propose an angle-based asymmetric sorting method, which operates with reduced computational overhead while effectively discerning the dominance relationship between solutions. This approach harmoniously balances the optimization speeds of both objectives, yielding solutions that significantly extend the operational lifespan of WSN. In comparison with analogous algorithms, our ASGA algorithm has been shown to increase the operational lifespan of WSN by 2.69%–3.68%.