Internet of Things (IoT) aims to provide ubiquitous services in real life. When different service requests arrive, how to assign them to proper service providers has become a challenging problem, especially in large-scale IoT service circumstances. In order to obtain the best service matching scheme, it is crucial to minimize total service cost and service time. Since both goals are conflicting, we have modeled IoT service as a multiobjective problem. Thus, we propose an improved decomposition-based multiobjective evolutionary algorithm for the IoT service (I-MOEA/D-IoTS). We have designed appropriate operators, such as array encoding, population initialization, Tchebycheff decomposition approach, local improvement, simulated binary crossover, and Gaussian mutation. In order to verify the effectiveness of the proposed algorithm, we apply it in three different scenarios of the agricultural IoT service. The simulation experimental results show that the proposed algorithm can achieve better tradeoff of solutions for IoT service and reduce total service cost and shorten service time.

中文翻译：

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一种改进的基于分解的物联网服务多目标进化算法


物联网（IoT）旨在提供现实生活中无处不在的服务。当不同的服务请求到达时，如何将其分配给合适的服务提供商已成为一个具有挑战性的问题，尤其是在大规模物联网服务情况下。为了获得最佳的服务匹配方案，最小化总服务成本和服务时间至关重要。由于这两个目标是相互冲突的，因此我们将物联网服务建模为多目标问题。因此，我们提出了一种改进的基于分解的物联网服务多目标进化算法（I-MOEA/D-IoTS）。我们设计了适当的算子，例如数组编码、种群初始化、切比雪夫分解方法、局部改进、模拟二元交叉和高斯变异。为了验证所提出算法的有效性，我们将其应用于农业物联网服务的三个不同场景。仿真实验结果表明，所提算法能够实现物联网服务解决方案的更好权衡，降低总服务成本并缩短服务时间。