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Hydrologic Cycle Optimization Part I: Background and Theory

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Advances in Swarm Intelligence (ICSI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10941))

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Abstract

A novel Hydrologic cycle Optimization (HCO) is proposed by simulating the natural phenomena of the hydrologic cycle on the earth. Three operators are employed in the algorithm: flow, infiltration, evaporation and precipitation. Flow step simulates the water flowing to lower areas and makes the population converge to better areas. Infiltration step executes neighborhood search. Evaporation and precipitation step could keep diversity and escape from local optima. The proposed algorithm is verified on ten benchmark functions and applied to a real-world problem named Nurse Scheduling Problem (NSP) with several comparison algorithms. Experiment results show that HCO performs better on most benchmark functions and in NSP than the comparison algorithms. In Part I, the background and theory of HCO are introduced firstly. And then, experimental studies on benchmark and real world problems are given in Part II.

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Acknowledgement

This work is supported by the National Natural Science Foundation (Grant No. 61703102, 71571120), Natural Science Foundation of Guangdong (Grant No. 2015A030310274, 2015A030313649), and Project of Department of Education of Guangdong Province (No. 2015KQNCX157). And the authors are very grateful to the anonymous reviewers for their valuable suggestions and comments to improve the quality of this paper.

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Authors and Affiliations

  1. School of Mechanical Engineering, Dongguan University of Technology, Dongguan, 523808, China

    Xiaohui Yan

  2. College of Management, Shenzhen University, Shenzhen, 518060, China

    Ben Niu

Authors
  1. Xiaohui Yan
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  2. Ben Niu
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Corresponding author

Correspondence to Ben Niu .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

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Yan, X., Niu, B. (2018). Hydrologic Cycle Optimization Part I: Background and Theory. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_33

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  • DOI: https://doi.org/10.1007/978-3-319-93815-8_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93814-1

  • Online ISBN: 978-3-319-93815-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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