A new optimizer using particle swarm theory¶
Why this mattered¶
TBD
Abstract¶
The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.
Related¶
- cite → THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS — Particle swarm optimization cites Fisher's iris measurement paper as a benchmark classification dataset for evaluating the optimizer's search performance.
- cite → Particle swarm optimization — A new optimizer using particle swarm theory builds directly on the original particle swarm optimization algorithm based on social swarm dynamics.
- enables → Particle swarm optimization — The earlier particle-swarm optimizer supplied the velocity-and-position update mechanism formalized and surveyed in the 2007 particle swarm optimization work.
- cite ← Particle swarm optimization — Particle swarm optimization builds on the original particle-swarm optimizer that updates candidate solutions using social and cognitive attraction terms.
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