Particle Swarm Optimization
Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.
Source record
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- Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. · DOI 10.1109/ICNN.1995.488968
- Shi, Y. & Eberhart, R. (1998). A Modified Particle Swarm Optimizer. IEEE Congress on Evolutionary Computation (CEC). · URL
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