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A novel dual-biological-community swarm intelligence algorithm with a commensal evolution strategy for multimodal problems

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Abstract

For sophisticated applications, engineers should always consider multi-objective, multi-task or multi-modal problems, especially in the Internet of Things, such as the data fusion of multi-sensor systems, multiple routing problems for automation and convergence control in wireless sensor networks. Normally, utilizing traditional methods costs abundant resources and yields the homogeneous results that fails to satisfy requirements. For the multimodal situation, this paper proposes the dual-biological-community swarm intelligence algorithm based on particle swarm optimization (DBC–PSO), which is combined with the commensal evolution strategy to enhance the convergence ability. This algorithm can split tasks into two communities and guarantee regional changes in information and search accuracy for multimodal problems through a commensal strategy. Moreover, some typical parameters, including the population rate, velocity and radius influence, are considered to study the algorithm performance. The performance is evaluated on 12 well-known multimodal problems, and the simulation is compared with some algorithms that utilize a similar evolutionary approach. The results indicate that the proposed algorithm exhibits strong performance and is very promising for use in more productive work.

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Acknowledgments

This paper is supported by the National Key Research and Development Funding (2018YFB1403703) and the Fundamental Research Funds for the Central Universities (CUC200D055).

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  1. State Key Laboratory of Media Convergence of Communication, School of Information and Communication Engineering, Communication University of China, No.1 Dingfuzhuang East Street, Chaoyang District, Beijing, 100024, People’s Republic of China

    Hui Ren, Xiaochen Shen & Xiaojun Jia

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Ren, H., Shen, X. & Jia, X. A novel dual-biological-community swarm intelligence algorithm with a commensal evolution strategy for multimodal problems. J Supercomput 77, 10850–10895 (2021). https://doi.org/10.1007/s11227-021-03721-8

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