What are the key differences between PSO and GA parameters?
PSO, or Particle Swarm Optimization, uses parameters like swarm size and inertia weight, while GA, or Genetic Algorithm, relies on crossover rate and mutation rate.
Can you explain how these parameters affect the optimization process?
Sure! In PSO, the inertia weight influences exploration vs. exploitation, while in GA, the crossover rate determines how solutions combine.
Which algorithm is generally more effective for complex problems?
It depends on the problem. PSO is often faster for continuous problems, while GA can be more effective for discrete optimization.
Are there scenarios where one is preferred over the other?
Yes, PSO is preferred for problems with smooth landscapes, while GA is better for rugged landscapes with many local optima.
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