macop.algorithms.base

Basic Algorithm class

Classes

Algorithm(initializer, evaluator, operators, …)

Abstract Algorithm class used as basic algorithm implementation with some specific initialization

class macop.algorithms.base.Algorithm(initializer, evaluator, operators, policy, validator, maximise=True, parent=None, verbose=True)

Abstract Algorithm class used as basic algorithm implementation with some specific initialization

This class enables to manage some common usages of operation research algorithms: - initialization function of solution - validator function to check if solution is valid or not (based on some criteria) - evaluation function to give fitness score to a solution - operators used in order to update solution during search process - policy process applied when choosing next operator to apply - callbacks function in order to do some relative stuff every number of evaluation or reload algorithm state - parent algorithm associated to this new algorithm instance (hierarchy management)

initializer

{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

verbose

{bool} – verbose or not information about the algorithm

currentSolution

{Solution} – current solution managed for current evaluation comparison

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

parent

{Algorithm} – parent algorithm reference in case of inner Algorithm instance (optional)

addCallback(_callback)

Add new callback to algorithm specifying usefull parameters

Parameters

_callback – {Callback} – specific Callback instance

end()

Display end message into run method

evaluate(solution)

Evaluate a solution using evaluator passed when intialize algorithm

Parameters

solution – {Solution} – solution to evaluate

Returns

{float} – fitness score of solution which is not already evaluated or changed

Note

if multi-objective problem this method can be updated using array of evaluator

getGlobalEvaluation()

Get the global number of evaluation (if inner algorithm)

Returns

{int} – current global number of evaluation

getGlobalMaxEvaluation()

Get the global max number of evaluation (if inner algorithm)

Returns

{int} – current global max number of evaluation

getParent()

Recursively find the main parent algorithm attached of the current algorithm

Returns

{Algorithm} – main algorithm set for this algorithm

increaseEvaluation()

Increase number of evaluation once a solution is evaluated for each dependant algorithm (parents hierarchy)

initRun()

Initialize the current solution and best solution using the initialiser function

isBetter(solution)

Check if solution is better than best found

• if the new solution is not valid then the fitness comparison is not done

• fitness comparison is done using problem nature (maximising or minimising)

Parameters

solution – {Solution} – solution to compare with best one

Returns

{bool} – True if better

progress()

Log progress and apply callbacks if necessary

resume()

Resume algorithm using Callback instances

run(evaluations)

Run the specific algorithm following number of evaluations to find optima

stop()

Global stopping criteria (check for parents algorithm hierarchy too)

update(solution)

Apply update function to solution using specific policy Check if solution is valid after modification and returns it

Parameters

solution – {Solution} – solution to update using current policy

Returns

{Solution} – updated solution obtained by the selected operator