opytimizer.optimizers.social.ssd¶
Social Ski Driver.
An SSD class, inherited from Optimizer.
This is the designed class to define SSD-related variables and methods.
References
A. Tharwat and T. Gabel. Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Computing and Applications (2019).
Initialization method.
Parameters: params – Contains key-value parameters to the meta-heuristics.
Exploration parameter.
Decay rate.
Array of local positions.
Array of velocities.
Compiles additional information that is used by this optimizer.
Parameters: space – A Space object containing meta-information.
Calculates the mean global solution (eq. 9).
Parameters: - alpha – 1st agent’s current position.
- beta – 2nd agent’s current position.
- gamma – 3rd agent’s current position.
Returns: Mean global solution.
Return type: (np.ndarray)
Updates a particle position (eq. 10).
Parameters: - position – Agent’s current position.
- index – Index of current agent.
Returns: A new position.
Return type: (np.ndarray)
Updates a particle velocity (eq. 11).
Parameters: - position – Agent’s current position.
- mean – Mean global best position.
- index – Index of current agent.
Returns: A new velocity.
Return type: (np.ndarray)
Evaluates the search space according to the objective function.
Parameters: - space – A Space object that will be evaluated.
- function – A Function object that will be used as the objective function.
Wraps Social Ski Driver over all agents and variables.
Parameters: - space – Space containing agents and update-related information.
- function – A Function object that will be used as the objective function.