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Introduction to Particle Swarm Optimization and Its Paradigms: A Bibliographic Survey

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Part of the book series: Studies in Big Data ((SBD,volume 86))

Abstract

Particle swarm optimization (PSO) belongs to evolutionary computation algorithms that are inspired by the swarming motion of living organisms. PSO has a humble beginning where it was only able to solve the single-objective continuous optimization problems. Since then through numerous refinements and contribution voiding weakness and combining elements of other methods proposing modifications, hybridizations, velocity update rules, and population topologies, PSO has matured with immense scope of real-world applications. Though swarm stagnation and dynamic environment are still identified as significant challenges nevertheless, continued interest in PSO shows no sign of slowing. More than ten thousand contributions in the IEEE database alone after 25 years of the first appearance is a strong indicator that the mentioned challenges will come to pass. This chapter enlists and discusses in brief the major developments in PSO along with the area of successful application in a concise form.

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Authors and Affiliations

  1. Department of Chemical Engineering, Dhofar University, Salalah, 211, Oman

    Mohd Shariq Khan

  2. Department of Chemical Engineering Technology, College of Applied Industrial Technology (CAIT), Jazan University, Jazan, 45971, Kingdom of Saudi Arabia

    Wahid Ali

  3. School of Chemical Engineering, Yeungnam University, Gyeongsan, 712-749, Republic of Korea

    Muhammad Abdul Qyyum & Moonyong Lee

  4. Department of Chemical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, 202001, Aligarh, India

    Khursheed B. Ansari

Authors
  1. Mohd Shariq Khan
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  2. Wahid Ali
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  3. Muhammad Abdul Qyyum
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  4. Khursheed B. Ansari
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  5. Moonyong Lee
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Correspondence to Mohd Shariq Khan .

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  1. National University of Singapore (NUS), BEARS, Singapore, Singapore

    Hasmat Malik

  2. Universiti Sultan Zainal Abidin (UniSZA), Kampung Gong Badak, Malaysia

    Nuzhat Fatema

  3. Wake Forest University, Winston Salem, NC, USA

    Jafar A. Alzubi

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Khan, M.S., Ali, W., Qyyum, M.A., Ansari, K.B., Lee, M. (2021). Introduction to Particle Swarm Optimization and Its Paradigms: A Bibliographic Survey. In: Malik, H., Fatema, N., Alzubi, J.A. (eds) AI and Machine Learning Paradigms for Health Monitoring System. Studies in Big Data, vol 86. Springer, Singapore. https://doi.org/10.1007/978-981-33-4412-9_6

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