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A novel hybrid algorithm based on PSO and FOA for target searching in unknown environments

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Abstract

In unknown environments, multiple-robot cooperation for target searching is a hot and difficult issue. Swarm intelligence algorithms, such as Particle Swarm Optimization (PSO) and Fruit Fly Optimization Algorithm (FOA), are widely used. To overcome local optima and enhance swarm diversity, this paper presents a novel multi-swarm hybrid FOA-PSO (MFPSO) algorithm for robot target searching. The main contributions of the proposed method are as follows. (1) The improved FOA (IFOA) provides a better value for the improved PSO (IPSO) to find the next optimal robot position value. (2) Multi-swarm strategy is introduced to enhance the diversity and achieve an effective exploration to avoid premature convergence and falling into local optima. (3) An escape mechanism named MSCM (Multi-Scale Cooperative Mutation) is used to address the limitation of local optima and enhance the escape ability for obstacle avoidance. All of the aspects mentioned above lead robots to the target without falling into local optima and allow the search mission to be performed more quickly. Several experiments in four parts are performed to verify the better performance of MFPSO. The experimental results show that the performance of MFPSO is much more significant than that of other current approaches.

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Acknowledgments

The authors thank the researcher Mr. Dadgar for providing the “Robotic Target Searching Simulator” for free. This work was supported by the National Natural Science Foundation of China (U1813205 and 61573135), the Independent Research Project of State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body (71765003), the Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing Open Foundation (2017TP1011), the Planned Science and Technology Project of Hunan Province (2016TP1023), Key Research and Development Project of Science and Technology Plan of Hunan Province (2018GK2021), the Science and Technology Plan Project of Shenzhen City (JCYJ20170306141557198), and the Key Project of Science and Technology Plan of Changsha City (kq1801003).

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Authors and Affiliations

  1. State Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, China

    Hongwei Tang, Wei Sun & Hongshan Yu

  2. Hunan Provincial Key Laboratory of Grids Operation and Control on Multi-Power Sources Area, Shaoyang University, Shaoyang, 422000, China

    Hongwei Tang

  3. Hunan Provincial Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing, Hunan University, Changsha, 410082, China

    Hongwei Tang, Wei Sun, Hongshan Yu, Anping Lin, Min Xue & Yuxue Song

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  1. Hongwei Tang
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  2. Wei Sun
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  3. Hongshan Yu
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  5. Min Xue
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Correspondence to Wei Sun or Hongshan Yu.

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Tang, H., Sun, W., Yu, H. et al. A novel hybrid algorithm based on PSO and FOA for target searching in unknown environments. Appl Intell 49, 2603–2622 (2019). https://doi.org/10.1007/s10489-018-1390-0

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