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Dynamic multi-objective evolutionary algorithm for IoT services

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Abstract

The primary goal of the Internet of things(IoT) is to provide people with anywhere services in real life. But intelligent IoT shouldn’t only provide services, but also consider how to allocate heterogeneous resources reasonably, which has become a very challenging problem. To obtain the best resource allocation scheme, it is crucial to minimize the service cost and service time. Since the two objectives are contradictory, we have modelled IoT services as a dynamic multi-objective optimization problem. Then a dynamic multi-objective evolutionary algorithm for dynamic IoT services(dMOEA/DI) is proposed. In dMOEA/DI, we have designed operators such as the appropriate encoding method, dynamic detection operator, filtering strategy, differential evolution, and polynomial mutation. Based on the single service strategy and collaborative service strategy, experimental research is performed on the agricultural IoT services with dynamic requests under different distributions. The simulation experimental results prove that dMOEA/DI performs better than the contrasted algorithms on the IoT service optimization problems.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant nos. 61972456,41772123); Natural Science Foundation of Tianjin (No. 19JCYBJC15800).

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Authors and Affiliations

  1. School of Computer Science &technology, Tiangong University, Tianjin, 300387, China

    Shun-shun Fang & Zheng-yi Chai

  2. Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tianjin, 300387, China

    Shun-shun Fang & Zheng-yi Chai

  3. School of Electronic and Information Engineering, Tiangong University, Tianjin, 300387, China

    Ya-lun Li

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  1. Shun-shun Fang
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Fang, Ss., Chai, Zy. & Li, Yl. Dynamic multi-objective evolutionary algorithm for IoT services. Appl Intell 51, 1177–1200 (2021). https://doi.org/10.1007/s10489-020-01861-7

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