What is the Particle Swarm Optimization algorithm?
Particle Swarm Optimization (PSO) is a computational method used for optimizing a problem by iteratively improving candidate solutions.
How does PSO differ from other optimization algorithms?
Unlike genetic algorithms, PSO is inspired by social behavior of birds and uses a population of solutions that 'swarm' towards optimal solutions.
Can you explain how the PSO algorithm works step by step?
Sure! PSO starts with a group of random solutions (particles), evaluates their fitness, and then updates their positions based on their own experience and that of their neighbors.
What are some applications of PSO in real-world problems?
PSO is used in various fields such as engineering design, neural network training, and even in financial modeling.
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