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Knowledge and Information Systems

, Volume 58, Issue 1, pp 209–248 | Cite as

Complex-valued encoding symbiotic organisms search algorithm for global optimization

  • Fahui Miao
  • Yongquan ZhouEmail author
  • Qifang Luo
Regular Paper
  • 149 Downloads

Abstract

Symbiotic organisms search algorithm is a new meta-heuristic algorithm based on the symbiotic relationship between the biological which was proposed in recent years. In this paper, a novel complex-valued encoding symbiotic organisms search (CSOS) algorithm is proposed. The algorithm introduces the idea of complex coding diploid. Each individual is composed of real and imaginary parts and extends the search space from one dimension to two dimensions. This increases the diversity of the population, further enhances the ability of the algorithm to find the global optimal value, and improves the precision of the algorithm. CSOS has been tested with 23 standard benchmark functions and 2 engineering design problems. The results show that CSOS has better ability of finding global optimal value and higher precision.

Keywords

Symbiotic organisms search Complex-valued encoding Benchmark test functions Engineering problems 

Notes

Acknowledgements

This work is supported by National Science Foundation of China under Grant Nos. 61463007, 61563008. Project of Guangxi University for Nationalities Science Foundation under Grant No. 2016GXNSFAA380264.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information Science and EngineeringGuangxi University for NationalitiesNanningChina
  2. 2.Key Laboratories of Guangxi High Schools Complex System and Computational IntelligenceNanningChina

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