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This paper proposes an enhanced Non-Dominated Sorting Genetic Algorithm -II algorithm to optimize IoT service composition by incorporating national energy consumption requirements and user experience, areas often overlooked in traditional models that primarily focus on time, cost, and quality. The original NSGA-II algorithm is prone to premature convergence and local optima issues during population iteration. To address these limitations, we introduce a novel evaluation model and improve the elite retention strategy of the NSGA-II algorithm. The improved algorithm balances exploration and exploitation through dynamic crowding distance adjustment and adaptive selection pressure, enhancing diversity and avoiding local optima. Experimental results demonstrate that the I-NSGA algorithm not only reduces running time by 5.916% but also achieves a smoother Pareto surface, indicating a more optimal distribution of solutions. The novelty of this approach lies in its comprehensive inclusion of energy consumption and user experience, the timeliness in addressing emerging IoT optimization challenges, and the relevance to current IoT service composition needs. This validates the effectiveness and advancement of the proposed model and algorithm, providing a robust and efficient solution for IoT service composition optimization.
M. et al. (Wed,) studied this question.