Abstract
Optimal power allocation (OPA) is considered to be one of the key issues in designing a wireless sensor network (WSN). Generally, the OPA in WSN can be formulated as a numerical optimization problem with constraints. Differential evolution (DE) is a powerful evolutionary algorithm for numerical, however, the success of DE in solving a specific problem crucially depends on appropriately choosing suitable mutation strategy and its associated control parameter values. Meanwhile, there is no single parameter setting and strategy that is able to consistently obtain the best results for the OPA with different number of sensor nodes. Based on the above considerations, in this paper, a multi-operator based constrained differential evolution is proposed, where probability matching and constrained credit assignment techniques are used so as to adaptively select the most suitable strategy in different phase of the search process for the OPA. Additionally, the parameter adaptation technique is used to avoid the fine-tuning of DE parameters for different problems. The proposed algorithm has been evaluated in several OPA with different number of sensor nodes, and its performance is compared with single-strategy based DE variants and other methods. Experimental results indicate that the proposed algorithm is able to provide better results than the compared methods.
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The work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61573324, 61375066, and 61203307.
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Li, Y., Gong, W., Cai, Z. (2017). Optimal Power Allocation of Wireless Sensor Networks with Multi-operator Based Constrained Differential Evolution. In: Wagner, M., Li, X., Hendtlass, T. (eds) Artificial Life and Computational Intelligence. ACALCI 2017. Lecture Notes in Computer Science(), vol 10142. Springer, Cham. https://doi.org/10.1007/978-3-319-51691-2_29
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DOI: https://doi.org/10.1007/978-3-319-51691-2_29
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