def main(): print("RUN: JayaClasic") listVars = [VariableFloat(-10.0, 10.0) for i in range(30)] ja = JayaClasic(100, listVars, sumSquares) print(ja.run(100).getBestAndWorst()) print("--------------------------------------------------------------") print("RUN: Self-adaptive Jaya Algorithm") listVars = [VariableFloat(-10.0, 10.0) for i in range(30)] ja = JayaSelfAdaptive(listVars, sumSquares) print(ja.run(100).getBestAndWorst()) print("--------------------------------------------------------------") print("RUN: Quasi-oppositional Based Jaya (QO-Jaya) Algorithm") listVars = [VariableFloat(-10.0, 10.0) for i in range(30)] ja = JayaQuasiOppositional(100, listVars, sumSquares) print(ja.run(100).getBestAndWorst()) print("--------------------------------------------------------------") print("RUN: Self-adaptive Multi-population (SAMP) Jaya Algorithm") listVars = [VariableFloat(-10.0, 10.0) for i in range(30)] ja = JayaSAMP(100, listVars, sumSquares) print(ja.run(100).getBestAndWorst()) print("--------------------------------------------------------------") print("RUN: Self-adaptive Multi-population Elitist (SAMPE) Jaya " + "Algorithm MultiProcess") listVars = [VariableFloat(-10.0, 10.0) for i in range(30)] ja = JayaSAMPE(100, listVars, sumSquares) print(ja.run(100).getBestAndWorst()) print("--------------------------------------------------------------")
def main(): print("RUN: JayaClasic") listVars = [VariableFloat(-6.0, 6.0) for i in range(2)] ja = JayaClasic( 100, listVars, himmelblau, listConstraints=[himmelblauConstraintOne, himmelblauConstraintTwo]) print(ja.run(100).getBestAndWorst()) print("--------------------------------------------------------------") print("RUN: Self-adaptive Jaya Algorithm") listVars = [VariableFloat(-6.0, 6.0) for i in range(2)] ja = JayaSelfAdaptive( listVars, himmelblau, listConstraints=[himmelblauConstraintOne, himmelblauConstraintTwo]) print(ja.run(100).getBestAndWorst()) print("--------------------------------------------------------------") print("RUN: Quasi-oppositional Based Jaya (QO-Jaya) Algorithm") listVars = [VariableFloat(-6.0, 6.0) for i in range(2)] ja = JayaQuasiOppositional( 100, listVars, himmelblau, listConstraints=[himmelblauConstraintOne, himmelblauConstraintTwo]) print(ja.run(100).getBestAndWorst()) print("--------------------------------------------------------------") print("RUN: Self-adaptive Multi-population (SAMP) Jaya Algorithm") listVars = [VariableFloat(-6.0, 6.0) for i in range(2)] ja = JayaSAMP( 100, listVars, himmelblau, listConstraints=[himmelblauConstraintOne, himmelblauConstraintTwo]) print(ja.run(100).getBestAndWorst()) print("--------------------------------------------------------------") print( "RUN: Self-adaptive Multi-population Elitist (SAMPE) Jaya " + "Algorithm MultiProcess") listVars = [VariableFloat(-6.0, 6.0) for i in range(2)] ja = JayaSAMPE( 100, listVars, himmelblau, listConstraints=[himmelblauConstraintOne, himmelblauConstraintTwo]) print(ja.run(100).getBestAndWorst()) print("--------------------------------------------------------------")
def test_first_iteration(self): """Tests first iteration.""" listVars = [VariableFloat(-100.0, 100.0) for i in range(2)] ja = JayaClasic(5, listVars, sphere, population=self.population) bw = ja.run(1, self.rs1).getBestAndWorst() assert bw['best_value'] == 113.0 assert len(bw['best_solution']) == 2 assert all([a == b for a, b in zip(bw['best_solution'], [-8.0, 7.0])]) assert truncate(bw['worst_value'], 1) == 3997.7 assert len(bw['worst_solution']) == 2 assert all( [a == b for a, b in zip(bw['worst_solution'], [-44.12, 45.29])])
def test_second_iteration(self): """Tests second iteration.""" listVars = [VariableFloat(-100.0, 100.0) for i in range(2)] ja = JayaClasic(5, listVars, sphere, population=self.population) bw = ja.run(2, self.rs2).getBestAndWorst() assert truncate(bw['best_value'], 2) == 7.78 assert len(bw['best_solution']) == 2 assert truncate(bw['best_solution'][0], 3) == 2.787 assert truncate(bw['best_solution'][1], 3) == -0.097 assert truncate(bw['worst_value'], 2) == 2381.13 assert len(bw['worst_solution']) == 2 assert truncate(bw['worst_solution'][0], 3) == -37.897 assert truncate(bw['worst_solution'][1], 3) == 30.739
def test_third_iteration(self): """Tests third iteration.""" listVars = [VariableFloat(-5.12, 5.12) for i in range(2)] ja = JayaClasic(5, listVars, rastrigin, population=self.population) bw = ja.run(3, self.rs3).getBestAndWorst() assert truncate(bw['best_value'], 7) == 0.2150036 assert len(bw['best_solution']) == 2 assert truncate(bw['best_solution'][0], 9) == -0.031679095 assert truncate(bw['best_solution'][1], 10) == -0.0091367952 assert truncate(bw['worst_value'], 8) == 24.79038297 assert len(bw['worst_solution']) == 2 assert truncate(bw['worst_solution'][0], 9) == 2.415885711 assert truncate(bw['worst_solution'][1], 9) == -0.040158575
def test_second_iteration(self): """Tests second iteration.""" listVars = [VariableFloat(-5.12, 5.12) for i in range(2)] ja = JayaClasic(5, listVars, rastrigin, population=self.population) bw = ja.run(2, self.rs2).getBestAndWorst() assert truncate(bw['best_value'], 9) == 2.108371360 assert len(bw['best_solution']) == 2 assert truncate(bw['best_solution'][0], 9) == 0.982105143 assert truncate(bw['best_solution'][1], 9) == -0.974135755 assert truncate(bw['worst_value'], 8) == 26.25808866 assert len(bw['worst_solution']) == 2 assert truncate(bw['worst_solution'][0], 10) == 2.4303683434 assert truncate(bw['worst_solution'][1], 10) == -1.0337930258
def test_first_iteration(self): """Tests first iteration.""" listVars = [VariableFloat(-5.12, 5.12) for i in range(2)] ja = JayaClasic(5, listVars, rastrigin, population=self.population) bw = ja.run(1, self.rs1).getBestAndWorst() assert truncate(bw['best_value'], 9) == 2.108371360 assert len(bw['best_solution']) == 2 assert truncate(bw['best_solution'][0], 9) == 0.982105143 assert truncate(bw['best_solution'][1], 9) == -0.974135755 assert truncate(bw['worst_value'], 9) == 36.785779376 assert len(bw['worst_solution']) == 2 assert truncate(bw['worst_solution'][0], 2) == 5.12 assert truncate(bw['worst_solution'][1], 11) == -1.84339288493
def test_first_iteration(self): """Tests first iteration.""" listVars = [VariableFloat(-5.0, 5.0) for i in range(2)] ja = JayaClasic( 5, listVars, himmelblau, population=self.population, listConstraints=[himmelblauConstraintOne, himmelblauConstraintTwo]) bw = ja.run(1, self.rs1).getBestAndWorst() assert truncate(bw['best_value'], 3) == 11.890 assert len(bw['best_solution']) == 2 assert truncate(bw['best_solution'][0], 3) == 3.845 assert truncate(bw['best_solution'][1], 3) == -1.038 assert truncate(bw['worst_value'], 3) == 77.710 assert len(bw['worst_solution']) == 2 assert truncate(bw['worst_solution'][0], 3) == 0.191 assert truncate(bw['worst_solution'][1], 3) == 2.289
def test_first_iteration(self, monkeypatch): """Tests first iteration.""" def rn(): self.index += 1 return self.rn[self.index - 1] monkeypatch.setattr(np.random, 'rand', rn) listVars = [VariableFloat(-100.0, 100.0) for i in range(2)] ja = JayaClasic(5, listVars, sphere, population=self.population) bw = ja.run(1).getBestAndWorst() assert bw['best_value'] == 113.0 assert len(bw['best_solution']) == 2 assert all([a == b for a, b in zip(bw['best_solution'], [-8.0, 7.0])]) assert truncate(bw['worst_value'], 1) == 3997.7 assert len(bw['worst_solution']) == 2 assert all( [a == b for a, b in zip(bw['worst_solution'], [-44.12, 45.29])])
def test_second_iteration(self, monkeypatch): """Tests second iteration.""" def rn(): self.index += 1 return self.rn[self.index - 1] monkeypatch.setattr(np.random, 'rand', rn) listVars = [VariableFloat(-100.0, 100.0) for i in range(2)] ja = JayaClasic(5, listVars, sphere, population=self.population) bw = ja.run(2).getBestAndWorst() assert truncate(bw['best_value'], 2) == 7.78 assert len(bw['best_solution']) == 2 assert truncate(bw['best_solution'][0], 3) == 2.787 assert truncate(bw['best_solution'][1], 3) == -0.097 assert truncate(bw['worst_value'], 2) == 2381.13 assert len(bw['worst_solution']) == 2 assert truncate(bw['worst_solution'][0], 3) == -37.897 assert truncate(bw['worst_solution'][1], 3) == 30.739
def test_third_iteration(self, monkeypatch): """Tests third iteration.""" def rn(): self.index += 1 return self.rn[self.index - 1] monkeypatch.setattr(np.random, 'rand', rn) listVars = [VariableFloat(-5.12, 5.12) for i in range(2)] ja = JayaClasic(5, listVars, rastrigin, population=self.population) bw = ja.run(3).getBestAndWorst() assert truncate(bw['best_value'], 7) == 0.2150036 assert len(bw['best_solution']) == 2 assert truncate(bw['best_solution'][0], 9) == -0.031679095 assert truncate(bw['best_solution'][1], 10) == -0.0091367952 assert truncate(bw['worst_value'], 8) == 24.79038297 assert len(bw['worst_solution']) == 2 assert truncate(bw['worst_solution'][0], 9) == 2.415885711 assert truncate(bw['worst_solution'][1], 9) == -0.040158575
def test_second_iteration(self, monkeypatch): """Tests second iteration.""" def rn(): self.index += 1 return self.rn[self.index - 1] monkeypatch.setattr(np.random, 'rand', rn) listVars = [VariableFloat(-5.12, 5.12) for i in range(2)] ja = JayaClasic(5, listVars, rastrigin, population=self.population) bw = ja.run(2).getBestAndWorst() assert truncate(bw['best_value'], 9) == 2.108371360 assert len(bw['best_solution']) == 2 assert truncate(bw['best_solution'][0], 9) == 0.982105143 assert truncate(bw['best_solution'][1], 9) == -0.974135755 assert truncate(bw['worst_value'], 8) == 26.25808866 assert len(bw['worst_solution']) == 2 assert truncate(bw['worst_solution'][0], 10) == 2.4303683434 assert truncate(bw['worst_solution'][1], 10) == -1.0337930258
def test_first_iteration(self, monkeypatch): """Tests first iteration.""" def rn(): self.index += 1 return self.rn[self.index - 1] monkeypatch.setattr(np.random, 'rand', rn) listVars = [VariableFloat(-5.12, 5.12) for i in range(2)] ja = JayaClasic(5, listVars, rastrigin, population=self.population) bw = ja.run(1).getBestAndWorst() assert truncate(bw['best_value'], 9) == 2.108371360 assert len(bw['best_solution']) == 2 assert truncate(bw['best_solution'][0], 9) == 0.982105143 assert truncate(bw['best_solution'][1], 9) == -0.974135755 assert truncate(bw['worst_value'], 9) == 36.785779376 assert len(bw['worst_solution']) == 2 assert truncate(bw['worst_solution'][0], 2) == 5.12 assert truncate(bw['worst_solution'][1], 11) == -1.84339288493
def test_first_iteration(self, monkeypatch): """Tests first iteration.""" def rn(): self.index += 1 return self.rn[self.index - 1] monkeypatch.setattr(np.random, 'rand', rn) listVars = [VariableFloat(-5.0, 5.0) for i in range(2)] ja = JayaClasic( 5, listVars, himmelblau, population=self.population, listConstraints=[himmelblauConstraintOne, himmelblauConstraintTwo]) bw = ja.run(1).getBestAndWorst() assert truncate(bw['best_value'], 3) == 11.890 assert len(bw['best_solution']) == 2 assert truncate(bw['best_solution'][0], 3) == 3.845 assert truncate(bw['best_solution'][1], 3) == -1.038 assert truncate(bw['worst_value'], 3) == 77.710 assert len(bw['worst_solution']) == 2 assert truncate(bw['worst_solution'][0], 3) == 0.191 assert truncate(bw['worst_solution'][1], 3) == 2.289