SwarmOps Many Optimizing Liaisons (MOL)
SwarmOps algorithms are being removed and may not be compatible with future releases of Analyzer. Users should be cautious when using them in models
Note: SwarmOps algorithms are being removed and may not be compatible with future releases of Analyzer. Users should be cautious when using them in models
Version
SwarmOps 3.0
Description
SwarmOps MOL is a simplified version of SwarmOps PSO that considers only attraction to the best known design. Because it uses only the attraction to the global best design, it tends to converge quickly to a best design instead of exploring design space for other possibilities
More Information
This algorithm considers only the attraction to the best known design when computing particle velocity. The velocity update formula is:
Compared to the original PSO, it tends to converge quickly to a best design instead of exploring design space for other possibilities, because it uses only the attraction to the global best design.
References
SwarmOps Manual (p. 18)
Control Parameters
| Name | Default Value | Description |
|---|---|---|
| Optimization Parameters | ||
| Omega | 0.729 | Inertia weight. Option must have a value greater than -2. |
| Phi | 1.49445 | Weight on the attraction towards swarm's best position. Option must have a value greater than -4. |
| Seed | Seed for random number (optional). Specifying the same seed value between two different optimization runs would help generate identical results provided all other parameters stay the same. This is a good way to analyze the effect of different parameters on the optimization. | |
| SwarmSize | 50 | Number of agents or swarm size. Option must have a positive integer value. |
| Stopping Criteria | ||
| AbsoluteConvergenceTolerance | 1E-05 | Maximum absolute change in fitness value between successive evaluations to indicate convergence. The value specified must be greater than 0. |
| ConsecutiveFunctionEvaluations | 200 | The number of consecutive iterations for which the absolute or relative convergence criteria must be met to indicate convergence. Thus option must have a positive integer value. |
| MaxFunctionEvaluations | 1000 | The maximum number of iterations. Thus option must have a positive integer value. |