Base class for optimization algorithms.

This class defines the core interface for optimization strategies and keeps track of the cost history, evaluated solutions, and the best solution found. Subclasses must implement optimize and objective_function.

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Properties

cost_history

@property
def cost_history(self) -> List[float]

Returns the history of costs during optimization.

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solution_history

@property
def solution_history(self) -> List

Returns the history of evaluated solutions.

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best_solution

@property
def best_solution(self) -> Optional[Any]

Returns the best solution found so far, or None if unavailable.

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best_cost

@property
def best_cost(self) -> Optional[float]

Returns the cost of the best solution found so far, or None if unavailable.

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Functions

Function _record_best(...)

def _record_best(self, cost: float, best_solution: Any) -> None

Record a new cost value and update the best solution if improved.

Parameters:

• cost: float - Cost value to be added to the history.

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Function get_report()

def get_report(self) -> str

Generate a formatted summary report of the optimization process. The report includes the best solution, its associated cost, and the evolution of cost values per iteration.

Returns:

• report: str - A formatted string containing the optimization summary.

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Function register(...)

def register(self, alias: str, function: Callable[..., Any]) -> None

Register a function dynamically in the optimizer instance.

Parameters:

• alias: str - Name used to access the function as an attribute.
• function: Callable[..., Any] - Callable to be registered.

Raises:

• TypeError: If function is not callable.
• AttributeError: If alias is protected and cannot be modified, or if alias does not exist in the optimizer class.

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Function reset()

def reset(self)

Reset the object's internal state, clearing history and resetting values.

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Abstract methods

Function optimize(...)

def optimize(self, max_iters: int = 50, n_iter_no_change=10, verbose: bool = True) -> Any

Execute the optimization process. This method must be implemented by the subclass to define how the optimization strategy explores the search space.

Parameters:

• max_iters: int - Maximum number of iterations.
• n_iter_no_change: int, default=10 - The maximum number of iterations without updating the best solution.
• verbose: bool, default=True - Flag to enable or disable detailed output during optimization.

Implementation:

• Clonalg

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Function affinity_function(...)

def affinity_function(self, solution: Any) -> float

Evaluate the affinity of a candidate solution. This method must be implemented by the subclass to define the problem-specific.

Parameters:

• solution: Any - Candidate solution to be evaluated.

Returns:

• cost: float - Cost value associated with the given solution.