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An Improved Decomposition-Based Multiobjective Evolutionary Algorithm for IoT Service
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DOI：10.1109/JIOT.2020.3010834.png)
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Abstract
En 中文
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.
Keywords:
Optimization
Internet of Things
Sensors
Evolutionary computation
Quality of service
Immune system
Sociology
Decomposition
evolutionary algorithm
Internet of Things (IoT)
multiobjective optimization
service
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