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Learning–interaction–diversification framework for swarm intelligence optimizers: a unified perspective

  • Xianghua Chu
  • Teresa Wu
  • Jeffery D. Weir
  • Yuhui Shi
  • Ben Niu
  • Li Li
Original Article
  • 102 Downloads

Abstract

Due to the efficiency and efficacy in performance to tackle complex optimization problems, swarm intelligence (SI) optimizers, newly emerged as nature-inspired algorithms, have gained great interest from researchers over different fields. A large number of SI optimizers and their extensions have been developed, which drives the need to comprehensively review the characteristics of each algorithm. Hence, a generalized framework laid upon the fundamental principles from which SI optimizers are developed is crucial. This research takes a multidisciplinary view by exploring research motivations from biology, psychology, computing and engineering. A learning–interaction–diversification (LID) framework is proposed where learning is to understand the individual behavior, interaction is to describe the swarm behavior, and diversification is to control the population performance. With the LID framework, 22 state-of-the-art SI algorithms are characterized, and nine representative ones are selected to review in detail. To investigate the relationships between LID properties and algorithmic performance, LID-driven experiments using benchmark functions and real-world problems are conducted. Comparisons and discussions on learning behaviors, interaction relations and diversity control are given. Insights of the LID framework and challenges are also discussed for future research directions.

Keywords

Swarm intelligence Evolutionary algorithm Meta-heuristic algorithm Nature-inspired algorithm 

Notes

Acknowledgements

This work was partially supported by the Major Project for National Natural Science Foundation of China (Grant No. 71790615, the design for Decision-making System of National Security Management), the Key Project of National Nature Science Foundation of China (Grant No. 71431006, Decision Support Theory and Platform of the Embedded Service for Environmental Management), the National Natural Science Foundation of China (Grant No. 71501132, 71701079, 71571120, 71371127 and 61273367), the Natural Science Foundation of Guangdong Province (2016A030310067), and the 2016 Tencent “Rhinoceros Birds”—Scientific Research Foundation for Young Teachers of Shenzhen University.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

521_2018_3657_MOESM1_ESM.pdf (1.3 mb)
Supplementary material 1 (PDF 1374 kb)

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Xianghua Chu
    • 1
    • 2
  • Teresa Wu
    • 3
  • Jeffery D. Weir
    • 4
  • Yuhui Shi
    • 5
  • Ben Niu
    • 1
    • 2
  • Li Li
    • 1
    • 2
  1. 1.College of ManagementShenzhen UniversityShenzhenChina
  2. 2.Institute of Big Data Intelligent Management and DecisionShenzhen UniversityShenzhenChina
  3. 3.School of Computing, Informatics, Decision Systems EngineeringArizona State UniversityTempeUSA
  4. 4.School of Engineering and ManagementAir Force Institute of TechnologyWright Patterson AFBUSA
  5. 5.Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhenChina

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