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Everything about Particle swarm optimization

Iterative simulation method

In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a population of candidate solutions with regard to a given measure of quality. It solves a problem through interactions among a population of candidate solutions, dubbed particles, moving the particles around in the search-space according to simple mathematical formulae that adjust each particle's position and velocity. Each particle's movement is influenced by its own best known position so far, and by the best known position in its topological neighborhood ; vectors are updated as better positions are found. This is expected to move the swarm toward good solutions. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Particle swarm optimizationA 5-minute orientation
  2. Skim a recent paper: Particle Swarm Optimization Algorithm and Its Applications: A Systematic ReviewInfluential work since 2022
  3. Pick up a classic book: Particle swarm optimizationThe most-reprinted book on the subject
  4. Read the landmark paper: Particle swarm optimizationCited 4,244 times
  5. Go sideways: Meta-optimizationA closely related topic
  6. Go sideways: Multi-swarm optimizationA closely related topic

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🗂️ The subject at a glance

  1. Algorithm
  2. Parameter selection
  3. Neighbourhoods and topologies
  4. Inner workings
  5. Variants

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

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