Publication record · 18.cifr/1995.kennedy.pso
18.cifr/1995.kennedy.psoA concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and evolutionary computation are noted.
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Formal convergence guarantees were not established in the original paper; theoretical analysis of swarm dynamics remains open. Extensions to discrete and combinatorial spaces, as well as systematic parameter sensitivity studies (leading to inertia-weight and constriction-factor variants), were flagged as important next steps.