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Spherical search optimizer: a simple yet efficient meta-heuristic approach

  • Jie Zhao
  • Deyu TangEmail author
  • Zhen Liu
  • Yongming Cai
  • Shoubin Dong
Original Article
  • 104 Downloads

Abstract

In these years, more meta-heuristic approaches have been proposed inspired by nature. However, the search mode has not been researched deeply. In this paper, we find that search style and individual selection mechanism for interaction are the core problems for a meta-heuristic algorithm. In particular, we focus on search style and have studied the principle of basic hypercube search style and basic reduced hypercube search style. Inspired by them, we propose a spherical search style. Furthermore, we design a spherical search optimizer by the spherical search style and tournament selection method. And then, theoretical analysis of it is provided. To validate the performance of the proposed method, we compare our approach against nine state-of-the-art algorithms. The CEC2013, CEC2014, CEC2015 and CEC2017 suites and the data clustering optimization problem in the real world are used. Experimental results and analysis verify that it is a simple yet efficient method to solve continuous optimization problems.

Keywords

Meta-heuristic approach Hypercube search style Spherical search style Data clustering 

Notes

Acknowledgements

This work is supported by the Guang Dong Provincial Natural Fund Project (2016A030310300); the National Natural Science Foundation of China (71871069, 71401045, 61976239); the Ministry of Education in China Project of Humanities and Social Sciences (18YJAZH137); the Guangdong Provincial Natural Fund Project (2017A030313394); the major scientific research projects of Guangdong (2017WTSCX021); the planning project of the 13th Five-Year in Philosophy and Social Sciences of Guangzhou (2018GZGJ48); the Ministry of Education Science and Technology Development Center (2017A11001); and the Guangdong University Engineering Technology Research Center (2016GCZX004). This research was funded by the Guangdong Natural Science Foundation (Grant No. 2015A030308017) and the Guangdong Science and Technology Key Project (Grant No. 2015B010131009).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Medical Information and EngineeringGuangdong Pharmaceutical UniversityGuangzhouPeople’s Republic of China
  2. 2.School of Computer Science and EngineeringSouth China University of TechnologyGuangzhouPeople’s Republic of China
  3. 3.Department of Information Management Engineering, School of ManagementGuangdong University of TechnologyGuangzhouPeople’s Republic of China
  4. 4.Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research CenterGuangzhouPeople’s Republic of China

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