gradient_free_optimizers.optimizers.local_opt.repulsing_hill_climbing_optimizer

Complexity

Total Complexity

4

Size/Duplication

Total Lines	62
Duplicated Lines	0 %

Importance

Changes

0

3 Methods

Rating	Name	Duplication	Size	Complexity
A	RepulsingHillClimbingOptimizer.iterate()	0	7	1
A	RepulsingHillClimbingOptimizer.__init__()	0	28	1
A	RepulsingHillClimbingOptimizer.evaluate()	0	7	2

# Author: Simon Blanke
# Email: simon.blanke@yahoo.com
# License: MIT License


from . import HillClimbingOptimizer


class RepulsingHillClimbingOptimizer(HillClimbingOptimizer):
    name = "Repulsing Hill Climbing"
    _name_ = "repulsing_hill_climbing"
    __name__ = "RepulsingHillClimbingOptimizer"

    optimizer_type = "local"
    computationally_expensive = False

    def __init__(
        self,
        search_space,
        initialize={"grid": 4, "random": 2, "vertices": 4},
        constraints=[],
        random_state=None,
        rand_rest_p=0,
        nth_process=None,
        epsilon=0.03,
        distribution="normal",
        n_neighbours=3,
        repulsion_factor=5,
    ):
        super().__init__(
            search_space=search_space,
            initialize=initialize,
            constraints=constraints,
            random_state=random_state,
            rand_rest_p=rand_rest_p,
            nth_process=nth_process,
            epsilon=epsilon,
            distribution=distribution,
            n_neighbours=n_neighbours,
        )

        self.tabus = []
        self.repulsion_factor = repulsion_factor
        self.epsilon_mod = 1

    @HillClimbingOptimizer.track_new_pos
    def iterate(self):
        return self.move_climb(
            self.pos_current,
            epsilon=self.epsilon,
            distribution=self.distribution,
            epsilon_mod=self.epsilon_mod,
        )

    def evaluate(self, score_new):
        super().evaluate(score_new)

        if score_new <= self.score_current:
            self.epsilon_mod = self.repulsion_factor
        else:
            self.epsilon_mod = 1