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Evaluating PSO and MOPSO Equipped with Evolutionary Population Dynamics

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Optimisation Algorithms for Hand Posture Estimation

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

This section presents, discusses and analyses the results of the proposed improved PSO and MOPSO. A variety of test functions with different characteristics and difficulties are employed to efficiently benchmark the performance of the proposed PSO\(+\)EPD and MOPSO\(+\)EPD algorithms. The results are collected and presented quantitatively and qualitatively.

Part of this chapter has been reprinted from Shahrzad Saremi, Seyedali Mirjalili, Andrew Lewis, Alan Wee Chung Liew, Jin Song Dong: Enhanced multi-objective particle swarm optimisation for estimating hand postures, Knowledge-Based Systems, Volume 158, pp. 175–195, 2018 with permission from Elsevier.

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Authors and Affiliations

  1. Torrens University Australia, Fortitude Valley, Brisbane, QLD, Australia

    Shahrzad Saremi & Seyedali Mirjalili

Authors
  1. Shahrzad Saremi
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  2. Seyedali Mirjalili
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Correspondence to Shahrzad Saremi .

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Saremi, S., Mirjalili, S. (2020). Evaluating PSO and MOPSO Equipped with Evolutionary Population Dynamics. In: Optimisation Algorithms for Hand Posture Estimation. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-9757-8_4

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