Review:
Particle Swarm Optimization
overall review score: 4.5
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score is between 0 and 5
Particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
Key Features
- Population-based algorithm
- Inspired by social behavior
- Utilizes cooperative search
- Optimizes based on fitness function
Pros
- Efficiently optimized complex problems
- Easy to implement and understand
- Good convergence properties
Cons
- May get stuck in local optima
- Requires tuning of parameters
- Not suitable for all types of optimization problems