Multi-objective sensor network layout optimization method based on reference point sorting mechanism

Wireless sensor network (WSN) layout optimization requires the simultaneous improvement of coverage, energy balance, and network connectivity. However, conventional multi-objective optimization algorithms often suffer from uneven solution distributions and the loss of sparse or boundary solutions, which limits their performance in complex deployment environments. To address these limitations, this study proposes a Reference-Point-Based Multi-Objective Transit Search algorithm (R-MOTS) for WSN layout optimization. The main contribution of R-MOTS is the reconstruction of the diversity-preservation and environmental-selection mechanisms of the original MOTS by jointly integrating reference-point-guided sorting, individual density estimation, and adaptive columnar grid filtering. Specifically, reference-point-guided sorting improves the preservation of sparse and boundary solutions, density estimation suppresses excessive population crowding, and adaptive columnar grid filtering dynamically regulates the distribution of solutions across the objective space. In addition, an energy-weighted probabilistic sensing model and connectivity constraints are incorporated to formulate the WSN layout optimization problem in terms of coverage, energy consumption, and network connectivity. An energy-weighted probabilistic sensing model and connectivity constraints are further incorporated to formulate the WSN layout optimization problem. Experiments on ZDT1–ZDT3, DTLZ1–DTLZ2, and UF1 show that Reference-Point-Based Multi-Objective Transit Search (R-MOTS) obtains lower GD and IGD values and higher HV values than NSGA-II, MOEA/D, and the original MOTS. Under a high-obstacle-density condition of 30%, R-MOTS achieves an average coverage rate of 92.67%, an energy-balance index of 0.142, and a network lifetime of 1078 data-collection rounds. These results demonstrate that R-MOTS provides improved convergence, solution-set distribution, and deployment performance for complex WSN layout optimization.