Particle swarm optimization
(also PSO)
Particle swarm optimization definition
Particle Swarm Optimization is a computational method that optimizes problems by simulating social behaviors like those of birds in a flock. It involves particles representing potential solutions. They move through the problem space influenced by their best positions and the global best.
See also: backpropagation
History of particle swarm optimization
Particle Swarm Optimization (PSO) was developed in 1995 by social psychologist James Kennedy and Russell Eberhart, an electrical engineer. Inspired by the social behavior of birds and fish, their initial goal to simulate bird flock movement evolved into a method for solving complex optimization problems.
PSO models each solution as a particle in a swarm, collectively moving toward the best solution. Introduced at the 1995 IEEE International Conference on Neural Networks, PSO was initially compared to evolutionary algorithms like genetic algorithms. Over the years, it has been refined for efficiency and adapted for diverse applications.
Use cases of particle swarm optimization
- Network optimization. PSO is utilized to optimize routing paths in communication networks, ensuring efficient data transfer and minimal congestion.
- Engineering design optimization. PSO aids in designing more efficient and cost-effective structures, such as aerodynamic shapes, in the automotive and aerospace industries.
- Machine learning parameter tuning. PSO is employed in machine learning to fine-tune hyperparameters of algorithms, enhancing their accuracy and efficiency.