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

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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.

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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.

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  1. College of Management, Shenzhen University, Shenzhen, China

    Xianghua Chu, Ben Niu & Li Li

  2. Institute of Big Data Intelligent Management and Decision, Shenzhen University, Shenzhen, China

    Xianghua Chu, Ben Niu & Li Li

  3. School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287, USA

    Teresa Wu

  4. School of Engineering and Management, Air Force Institute of Technology, Wright Patterson AFB, OH, 45433, USA

    Jeffery D. Weir

  5. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Yuhui Shi

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  1. Xianghua Chu
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Chu, X., Wu, T., Weir, J.D. et al. Learning–interaction–diversification framework for swarm intelligence optimizers: a unified perspective. Neural Comput & Applic 32, 1789–1809 (2020). https://doi.org/10.1007/s00521-018-3657-0

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