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The sensor nodes in wireless sensor networks have the deficiency of limited energy, and the multi-hop transmission of information will lead to premature paralysis of nodes near the sink. The use of the mobile sink can balance the energy consumption and greatly prolong the lifetime. Therefore, this paper studies the scheduling strategy of multiple mobile sinks and proposes a heuristic strategy based on interval type-2 fuzzy rough neural network. The energy and lifetime of sensor nodes, as well as location information of the mobile sink and special nodes are taken as input features. Through neural network learning, the outputs of whether to move, moving direction, moving distance and residence time can complete the scheduling task. The scheduling problem is regarded as a multiobjective optimization problem, and the network lifetime, the moving path length and the network interpretability are optimized at the same time, so as to obtain a lightweight network with good interpretability and performance. Based on the parallel multiobjective evolutionary algorithm, a neural evolutionary framework is constructed. Compared with static sinks, random- moving sinks, sinks with manually designed strategy and gene expression programming-based sinks as well as other state- of-the-art multiobjective evolutionary algorithms, the proposed framework can achieve superior results. IEEE