FIND THE OPTIMIZE X BASED ON THE SIMULATED ANNEALING¶
Finds the optimal X value on a curve using the Simulated Annealing metaheuristic algorithm. Accepts a 2-D curve (X/Y points) and searches for the X that minimizes or maximizes the Y value (or drives it toward a target), making it useful for non-convex, noisy objective landscapes where gradient-based methods fail.
When to use¶
Classification: process.
Tagged: curve, maximize, metaheuristic, minimize, minmax, normalization, optimization, simulated_annealing.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| Curve | curve | vector | — | Input 2-D curve (X/Y point pairs) over which the optimizer will search for the optimal X value; must contain at least two points. | |
| Number Of Iterations | num_iterations | scalar | 100 | Total number of simulated annealing iterations to perform; higher values improve solution quality at the cost of runtime (default: 100). | |
| Step Size | step_size | scalar | 0.01 | Perturbation step size applied to X at each iteration; smaller values give finer local search while larger values allow broader exploration (default: 0.01). | |
| Initial Temperature | initial_temperature | scalar | 10.0 | Starting temperature for the annealing schedule; higher values increase the probability of accepting worse solutions early, promoting global search (default: 10.0). | |
| Data Normalization Type | normalize | scalar | minmax | Normalization method applied to the curve data before optimization: ‘minmax’ (0–1 scaling), ‘znorm’ (zero-mean unit-variance), or ‘false’ to skip normalization (default: ‘minmax’). | |
| Target Value | target_value | scalar | 0 | Target Y value the optimizer tries to reach; set to 0 (default) when purely minimizing or maximizing without a specific target. | |
| Objective | objective | scalar | minimize | Optimization direction: ‘minimize’ to find the X yielding the lowest Y, or ‘maximize’ to find the X yielding the highest Y (default: ‘minimize’). |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| curve_simulated_annealing_optimizer_output_1 | curve_simulated_annealing_optimizer_output_1 | scalar | Optimal X value found by the simulated annealing algorithm — the point on the input curve that best satisfies the specified objective (minimize/maximize/target). |
Disciplines¶
- data.curve.transform
- design_exploration.optimization
Runnable example¶
A runnable example is registered for this worker. Open the example workflow on the d3VIEW canvas: /api/workflow/example?id=curve_simulated_annealing_optimizer
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