pyJaya.variants package

pyJaya.variants.base module

class pyJaya.variants.base.JayaBase(numSolutions, listVars, functionToEvaluate, listConstraints=[], population=None)[source]

Bases: object

Jaya base class

Parameters

  • numSolutions (int) – Number of solutions of population.

  • listVars (list) – Range list.

  • functionToEvaluate (funtion) – Function to minimize or maximize.

  • listConstraints (list, optional) – Constraint list. Defaults to [].

  • population (Population, optional) – Population. Defaults to None.

addConstraint(constraintFuntion)[source]

Add constraint

Parameters

constraintFuntion (funtion) – Funtion to add as constraint.

generatePopulation()[source]

Generate population

Returns

Population generated.

Return type

Population

generate_rn(number_iterations)[source]

Generate random numbers

getBestAndWorst()[source]

Best and worst value and solution

Returns

Best value, worst value, best solution and worst solution.

Return type

dict

run(number_iterations, rn=[])[source]

Client must define it self

toMaximize()[source]

Change to maximize funtion.

pyJaya.variants.binary module

class pyJaya.variants.binary.JayaBinary(numSolutions, listVars, functionToEvaluate, listConstraints=[], population=None)[source]

Bases: pyJaya.variants.base.JayaBase

Jaya binary class

Parameters

  • numSolutions (int) – Number of solutions of population.

  • listVars (list) – Range list.

  • functionToEvaluate (funtion) – Function to minimize or maximize.

  • listConstraints (list, optional) – Constraint list. Defaults to [].

  • population (Population, optional) – Population. Defaults to None.

run(number_iterations)[source]

Run method

Parameters

number_iterations (int) – Number of iterations.

Returns

Final population.

Return type

Population

pyJaya.variants.clasic module

class pyJaya.variants.clasic.JayaClasic(numSolutions, listVars, functionToEvaluate, listConstraints=[], population=None)[source]

Bases: pyJaya.variants.base.JayaBase

Jaya clasic class

Parameters

  • numSolutions (int) – Number of solutions of population.

  • listVars (list) – Range list.

  • functionToEvaluate (funtion) – Function to minimize or maximize.

  • listConstraints (list, optional) – Constraint list. Defaults to [].

  • population (Population, optional) – Population. Defaults to None.

run(number_iterations, rn=[])[source]

Run method

Parameters

number_iterations (int) – Number of iterations.

Returns

Final population.

Return type

Population

pyJaya.variants.quasiOppositional module

class pyJaya.variants.quasiOppositional.JayaQuasiOppositional(numSolutions, listVars, functionToEvaluate, listConstraints=[], population=None)[source]

Bases: pyJaya.variants.base.JayaBase

Jaya clasic class

Parameters

  • numSolutions (int) – Number of solutions of population.

  • listVars (list) – Range list.

  • functionToEvaluate (funtion) – Function to minimize or maximize.

  • listConstraints (list, optional) – Constraint list. Defaults to [].

  • population (Population, optional) – Population. Defaults to None.

generateQuasiOpposite(population)[source]

Generate quasi-opposite population

Parameters

population (Population) – Population to generate quasi-opposite.

Returns

[description]

Return type

Population

newPopulation()[source]

New population with quasi-opposite elements.

Returns

Population with quasi-opposite elements.

Return type

Population

run(number_iterations, rn=[])[source]

Run method

Parameters

number_iterations (int) – Number of iterations.

Returns

Final population.

Return type

Population

pyJaya.variants.samp module

class pyJaya.variants.samp.JayaSAMP(numSolutions, listVars, functionToEvaluate, listConstraints=[], population=None)[source]

Bases: pyJaya.variants.base.JayaBase

Jaya SAMP class

Parameters

  • numSolutions (int) – Number of solutions of population.

  • listVars (list) – Range list.

  • functionToEvaluate (funtion) – Function to minimize or maximize.

  • listConstraints (list, optional) – Constraint list. Defaults to [].

  • population (Population, optional) – Population. Defaults to None.

run(number_iterations, rn=[])[source]

Run method

Parameters

number_iterations (int) – Number of iterations.

Returns

Final population.

Return type

Population

sprint(population)[source]

Jaya clasic to sub-population

Parameters

population (Population) – Population to evaluate whit Jaya clasic.

Returns

Sprint final population.

Return type

Population

pyJaya.variants.sampe module

class pyJaya.variants.sampe.JayaSAMPE(numSolutions, listVars, functionToEvaluate, listConstraints=[], population=None)[source]

Bases: pyJaya.variants.base.JayaBase

Jaya SAMPE class

Parameters

  • numSolutions (int) – Number of solutions of population.

  • listVars (list) – Range list.

  • functionToEvaluate (funtion) – Function to minimize or maximize.

  • listConstraints (list, optional) – Constraint list. Defaults to [].

  • population (Population, optional) – Population. Defaults to None.

run(number_iterations)[source]

Run method

Parameters

number_iterations (int) – Number of iterations.

Returns

Final population.

Return type

Population

sprint(population)[source]

Jaya clasic to sub-population

Parameters

population (Population) – Population to evaluate whit Jaya clasic.

Returns

Sprint final population.

Return type

Population

class pyJaya.variants.sampe.MultiprocessJayaSAMPE(numSolutions, listVars, functionToEvaluate, listConstraints=[], population=None)[source]

Bases: pyJaya.variants.base.JayaBase

Multiprocess Jaya SAMPE class

Parameters

  • numSolutions (int) – Number of solutions of population.

  • listVars (list) – Range list.

  • functionToEvaluate (funtion) – Function to minimize or maximize.

  • listConstraints (list, optional) – Constraint list. Defaults to [].

  • population (Population, optional) – Population. Defaults to None.

generate(m)[source]

Generate population

Parameters

m (int) – Number of groups based on the quality of the solutions.

Returns

Generated population.

Return type

Population

run(number_iterations)[source]

Run method

Parameters

number_iterations (int) – Number of iterations.

Returns

Final population.

Return type

Population

sprint(population)[source]

Jaya clasic to sub-population

Parameters

population (Population) – Population to evaluate whit Jaya clasic.

Returns

Sprint final population.

Return type

Population

static worker(sampe, population)[source]

Worker method

Parameters

population (Population) – Original population.

Returns

Generated population by sprint.

Return type

Population

pyJaya.variants.selfAdaptive module

class pyJaya.variants.selfAdaptive.JayaSelfAdaptive(listVars, functionToEvaluate, listConstraints=[], population=None)[source]

Bases: pyJaya.variants.base.JayaBase

Jaya Self-adaptive class

Parameters

  • listVars (list) – Range list.

  • functionToEvaluate (funtion) – Function to minimize or maximize.

  • listConstraints (list, optional) – Constraint list. Defaults to [].

nextPopulation(population)[source]

New population.

Returns

Next population.

Return type

Population

run(number_iterations, rn=[])[source]

Run method

Parameters

number_iterations (int) – Number of iterations.

Returns

Final population.

Return type

Population