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Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review

📅 April 19, 2022 👤 Ahmed G. Gad 📖 Archives of Computational Methods in Engineering 📊 1,724 citations

🤖 Plain-English Summary

Abstract Throughout the centuries, nature has been a source of inspiration, with much still to learn from and discover about. Some technical characteristics, including accuracy, evaluation environments, and proposed case study are involved to investigate the effectiveness of different PSO methods and applications.

🔑 Key Findings

  • Among many others, Swarm Intelligence (SI), a substantial branch of Artificial Intelligence, is built on the intelligent collective behavior of social swarms in nature.
  • One of the most popular SI paradigms, the Particle Swarm Optimization algorithm (PSO), is presented in this work.
  • Many changes have been made to PSO since its inception in the mid 1990s.

💡 Why This Matters

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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📋 Article Details

Category 🤖 Artificial Intelligence
Published Apr 19, 2022
Journal Archives of Computational Methods in Engineering
Authors Ahmed G. Gad
DOI 10.1007/s11831-021-09694-4
Citations 1,724
Source OpenAlex

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