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Monkey King Evolution: an enhanced ebb-tide-fish algorithm for global optimization and its application in vehicle navigation under wireless sensor network environment

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Abstract

Optimization algorithms are proposed to maximize the desirable properties while simultaneously minimizing the undesirable characteristics. Particle Swarm Optimization (PSO) is a famous optimization algorithm, and it has undergone many variants since its inception in 1995. Though different topologies and relations among particles are used in some state-of-the-art PSO variants, the overall performance on high dimensional multimodal optimization problem is still not very good. In this paper, we present a new memetic optimization algorithm, named Monkey King Evolutionary (MKE) algorithm, and give a comparative view of the PSO variants, including the canonical PSO, Inertia Weighted PSO, Constriction Coefficients PSO, Fully-Informed Particle Sawrm, Cooperative PSO, Comprehensive Learning PSO and some variants proposed in recent years, such as Dynamic Neighborhood Learning PSO, Social Learning Particle Swarm Optimization etc. The proposed MKE algorithm is a further work of ebb-tide-fish algorithm and what’s more it performs very well not only on unimodal benchmark functions but also on multimodal ones on high dimensions. Comparison results under CEC2013 test suite for real parameter optimization show that the proposed MKE algorithm outperforms state-of-the-art PSO variants significantly. An application of the vehicle navigation optimization is also discussed in the paper, and the conducted experiment shows that the proposed approach to path navigation optimization saves travel time of real-time traffic navigation in a micro-scope traffic networks.

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Notes

  1. The work mentioned in this manuscript is an earlier work and also submitted earlier than the article [23] published in Knowledge-based Systems. The difference and main contribution of the manuscript is to give some clues for algorithm enhancement. The relation between a former weaker algorithm ebb-tide-fish algorithm and the more powerful algorithm onkey king evolutionary algorithm is shown herein the paper. Another contribution of the paper is the adaptively use of Monkey King Evolutionary (MKE) algorithm for tacking vehicle navigation problem under wireless sensor network environment.

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Acknowledgments

The authors extend their appreciation to National Natural Science Foundation of China (61273290) for funding this work.

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Authors and Affiliations

  1. Fuzhou University of International Studies and Trade, Fuzhou, China

    Jeng-Shyang Pan

  2. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Zhenyu Meng

  3. School of Computer Science, Engineering and Mathematics, Flinders University, Adelaide, Australia

    Shu-Chuan Chu

  4. Department of Computer Science and Technology, Xiamen University of Technology, Xiamen, China

    Hua-Rong Xu

Authors
  1. Jeng-Shyang Pan
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  2. Zhenyu Meng
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  3. Shu-Chuan Chu
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  4. Hua-Rong Xu
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Correspondence to Zhenyu Meng.

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Pan, JS., Meng, Z., Chu, SC. et al. Monkey King Evolution: an enhanced ebb-tide-fish algorithm for global optimization and its application in vehicle navigation under wireless sensor network environment. Telecommun Syst 65, 351–364 (2017). https://doi.org/10.1007/s11235-016-0237-4

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