Particle Swarm Optimization (PSO) in MATLAB || Shortest Path in MATLAB using PSO
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Particle Swarms Optimization:
Particle Swarm Optimization was proposed by Kennedy and Eberhart in 1995. As sociobiologists believe a school of fish or a flock of birds that moves in a group “can profit from the experience of all other members”. In other words, while a bird flying and searching randomly for food, for instance, all birds in the flock can share their discovery and help the entire flock get the best hunt.
While we can simulate the movement of a flock of birds, we can also imagine each bird is to help us find the optimal solution in a high-dimensional solution space and the best solution found by the flock is the best solution in the space.
This is a heuristic solution because we can never prove the real global optimal solution can be found and it is usually not. However, we often find that the solution found by PSO is quite close to the global optimal.
Further Readings:
These are the original papers that proposed the particle swarm optimization, and the early research on refining its hyperparameters:
• Kennedy J. and Eberhart R. C. Particle swarm optimization. In Proceedings of the International Conference on Neural Networks; Institute of Electrical and Electronics Engineers. Vol. 4. 1995. pp. 1942–1948. DOI: 10.1109/ICNN.1995.488968
• Eberhart R. C. and Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization. In Proceedings of the 2000 Congress on Evolutionary Computation (CEC ‘00). Vol. 1. 2000. pp. 84–88. DOI: 10.1109/CEC.2000.870279
• Shi Y. and Eberhart R. A modified particle swarm optimizer. In Proceedings of the IEEE International Conferences on Evolutionary Computation, 1998. pp. 69–73. DOI: 10.1109/ICEC.1998.699146
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