12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091 |
- """MOEAD sub problem algorithm class
- """
- # main imports
- import logging
- # module imports
- from ..Algorithm import Algorithm
- class MOSubProblem(Algorithm):
- """Specific MO sub problem used into MOEAD
- Attributes:
- index: {int} -- sub problem index
- weights: {[float]} -- sub problems objectives weights
- initalizer: {function} -- basic function strategy to initialize solution
- evaluator: {function} -- basic function in order to obtained fitness (mono or multiple objectives)
- operators: {[Operator]} -- list of operator to use when launching algorithm
- policy: {Policy} -- Policy class implementation strategy to select operators
- validator: {function} -- basic function to check if solution is valid or not under some constraints
- maximise: {bool} -- specify kind of optimisation problem
- currentSolution: {Solution} -- current solution managed for current evaluation
- bestSolution: {Solution} -- best solution found so far during running algorithm
- callbacks: {[Callback]} -- list of Callback class implementation to do some instructions every number of evaluations and `load` when initializing algorithm
- """
- def __init__(self,
- index,
- weights,
- initalizer,
- evaluator,
- operators,
- policy,
- validator,
- maximise=True,
- parent=None):
- super().__init__(initalizer, evaluator, operators, policy,
- validator, maximise, parent)
- self._index = index
- self._weights = weights
- def run(self, evaluations):
- """
- Run the local search algorithm
- Args:
- evaluations: {int} -- number of evaluations
-
- Returns:
- {Solution} -- best solution found
- """
- # by default use of mother method to initialize variables
- super().run(evaluations)
- # initialize solution if necessary
- if self._bestSolution is None:
- self.initRun()
- # new operators list keep track of current sub problem
- for op in self._operators:
- op.setAlgo(self)
- for _ in range(evaluations):
- # keep reference of sub problem used
- self._policy.setAlgo(self)
- # update solution using policy
- newSolution = self.update(self._bestSolution)
- # if better solution than currently, replace it
- if self.isBetter(newSolution):
- self._bestSolution = newSolution
- # increase number of evaluations
- self.increaseEvaluation()
- self.progress()
- # stop algorithm if necessary
- if self.stop():
- break
- logging.info(f"---- Current {newSolution} - SCORE {newSolution.fitness()}")
- logging.info(f"End of {type(self).__name__}, best solution found {self._bestSolution}")
- return self._bestSolution
|