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A robust seasons algorithm to mitigate the search complexity of ES-PTS in finding phase weighting factors

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Abstract

This paper presents a computationally efficient and robust evolutionary algorithm to find the better permutation of weighting phase factors in minimizing envelope fluctuations of orthogonal frequency division multiplexing signals. The proposed optimization method is called the seasons algorithm, in which its main inspiration is the growth and survival of trees in nature. This algorithm formulates fluctuation reduction as an optimization problem. It is combined with the partial transmit sequence method to decreases both the large fluctuations of signals and the search cost for larger sub-blocks at the same time. The search complexity of the proposed hybrid algorithm is polynomial, while the complexity of the exhaustive search partial transmit sequence scheme increases exponentially with the number of sub-blocks. The proposed algorithm is evaluated using different benchmarks and compared with several counterpart methods according to the fluctuation reduction performance and search cost. The simulation results show that the proposed algorithm outperformed the existing optimization meta-heuristics in minimizing the envelop fluctuations.

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Authors and Affiliations

  1. Department of Computer Engineering, University of Bonab, Bonab, Iran

    Hojjat Emami

  2. Department of Electrical Engineering, University of Bonab, Bonab, Iran

    Abbas Ali Sharifi

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  1. Hojjat Emami
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  2. Abbas Ali Sharifi
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Correspondence to Hojjat Emami.

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Emami, H., Sharifi, A.A. A robust seasons algorithm to mitigate the search complexity of ES-PTS in finding phase weighting factors. Soft Comput 25, 11405–11419 (2021). https://doi.org/10.1007/s00500-021-05944-6

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