| 1. | Convergence to a local optimum has been analyzed for PSO in and.
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| 2. | It is not guaranteed to even be a local optimum.
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| 3. | It might instead find a local optimum value, not the global optimum.
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| 4. | The algorithm ends after reaching some local optimum.
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| 5. | This will converge to a local optimum, so multiple runs may produce different results.
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| 6. | It has been proven that PSO need some modification to guarantee to find a local optimum.
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| 7. | An optimal solution is one that is a local optimum, but possibly not a global optimum.
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| 8. | Finding an arbitrary local optimum is relatively straightforward by using classical " local optimization " methods.
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| 9. | Gradient-based methods find local optima with high reliability but are normally unable to escape a local optimum.
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| 10. | It does however only find a local optimum, and is commonly run multiple times with different random initializations.
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