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A New Adaptive Differential Evolution Algorithm Fused with Multiple Strategies for Robot Path Planning

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Abstract

In a complex environment with multiple feasible paths, planning a globally optimal path and performing dynamic path planning for robots is challenging. Therefore, this paper proposed a new adaptive Differential Evolution algorithm combined with K-modes clustering, BP neural network, and other strategies (KBPDE) to overcome this issue. The proposed KBPDE algorithm applies a new population initialization strategy based on K-modes clustering and a two-population strategy to enhance the population diversity and prevent the algorithm from falling into local optima. A BP neural network model is developed to obtain the appropriate mutation scale factor F at each generation G. Based on the positional relationships among individuals in the current generation, a novel mutation strategy is presented. Finally, an adaptive population size strategy is introduced to raise the algorithm’s efficiency. In a complicated environment with several feasible paths, the experimental results demonstrate that KBPDE can obtain the globally optimal path in contrast to GA, DE, and the excellent versions of DE. In a partially known environment, the EKBPDE can plan the path successfully.

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Data availability

The data that support the findings of this study are available from the corresponding author, [author initials], upon reasonable request.

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Funding

This research was funded by the Guangxi Science and Technology Program(AB21120039).

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Author notes

  1. Likun Hu and Zhihuan Ma have contributed equally to this work.

Authors and Affiliations

  1. College of Electrical Engineering, Guangxi University, Xixiangtang, Nanning, 530004, Guangxi, China

    Yueyang Liu, Likun Hu & Zhihuan Ma

  2. College of Electrical Engineering, Guangxi University, Xixiangtang, Nanning, 610101, Guangxi, China

    Likun Hu

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  1. Yueyang Liu
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Contributions

Yueyang Liu performed the study conception and design, the data analysis and the manuscript writing; Likun Hu performed the review and revision of manuscripts; Zhihuan Ma given some advice for this research.All authors read and approved the final manuscript.

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Correspondence to Yueyang Liu.

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Liu, Y., Hu, L. & Ma, Z. A New Adaptive Differential Evolution Algorithm Fused with Multiple Strategies for Robot Path Planning. Arab J Sci Eng (2023). https://doi.org/10.1007/s13369-023-08380-w

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