carbatpy.optimizations.opti_de

Functions

optimize(mode, config, boundaries[, same_fluid, ...])

Perform the global optimization for the selected cycle.

Module Contents

carbatpy.optimizations.opti_de.optimize(mode: str, config: dict | str, boundaries: dict, same_fluid: bool = True, heat_losses: float = 0.0, COP: float | None = None, q_dot: float | None = None, verbose: bool = True, n_processes: int | None = None, maxtasksperchild: int = 20, n_ieq_constr: int = 3, pop_size: int = 50, sampling_iterations: int = 50, de_variant: str = 'DE/rand/1/bin', CR: float = 0.3, dither: str = 'vector', jitter: bool = False, n_gen: int = 100, period: int = 20, ftol: float = 0.0001, return_least_infeasible: bool = False)[source]

Perform the global optimization for the selected cycle.

This function sets up the optimization problem, initializes the differential evolution algorithm, handles parallelization, and executes the optimization using Pymoo.

Parameters:

  • mode (str) – Optimization mode. Must be one of 'hp', 'orc', or 'cb'.

  • config (dict | str) – Configuration dictionary or path to configuration file.

  • boundaries (dict) – Lower and upper bounds for all optimization variables.

  • same_fluid (bool) – Whether the working fluid is the same for all cycles. Only used in 'cb' mode. Defaults to True.

  • heat_losses (float) – Heat losses fraction. Only used in 'cb' mode. Defaults to 0.0.

  • COP (float | None) – Coefficient of performance. Only used in 'orc' mode.

  • q_dot (float | None) – Heat input/output rate. Only used in 'orc' mode.

  • verbose (bool) – Whether to print detailed optimization progress. Defaults to True.

  • n_processes (int | None) – Number of processes for parallelization. Defaults to auto-detection via default_n_processes().

  • maxtasksperchild (int) – Maximum tasks per worker process in the pool. Defaults to 20.

  • n_ieq_constr (int) – Number of inequality constraints. Defaults to 3.

  • pop_size (int) – Population size for the DE algorithm. Defaults to 50.

  • sampling_iterations (int) – Number of iterations for Latin Hypercube sampling. Defaults to 50.

  • de_variant (str) – Variant of the differential evolution algorithm. Defaults to 'DE/rand/1/bin'.

  • CR (float) – Crossover probability for DE. Defaults to 0.3.

  • dither (str) – Dithering strategy for DE. Defaults to 'vector'.

  • jitter (bool) – Whether to apply jitter in DE. Defaults to False.

  • n_gen (int) – Maximum number of generations. Defaults to 100.

  • period (int) – Period for robust termination checking. Defaults to 20.

  • ftol (float) – Function tolerance for termination. Defaults to 1e-4.

  • return_least_infeasible (bool) – Whether to return the least infeasible solution if no feasible solution is found. Defaults to False.

Raises:

ValueError – If boundaries is None or required parameters for the selected mode are missing.

Returns:

The optimization result object containing the best solution and additional metadata.

Return type:

pymoo.core.result.Result