Update v2.6.0

README.md

@@ -4,7 +4,7 @@ The Genetic and Evolutionary Algorithm Toolbox for Python with high performance.
 ![Travis](https://travis-ci.org/geatpy-dev/geatpy.svg?branch=master)
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-![Pypi](https://img.shields.io/badge/pypi-2.5.1-blue.svg)
+![Pypi](https://img.shields.io/badge/pypi-2.6.0-blue.svg)
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@@ -40,17 +40,17 @@ The features of Geatpy:
 
 * Many evaluation metrics of algorithms.
 
-## Improvement of Geatpy 2.5.1
+## Improvement of Geatpy 2.6.0
 
-* Support setting random seed in Geatpy.
+* Add Push and Pull Search Strategy for MOEA/D-DE.
 
-* The kernel of Geatpy is more stable.
+* Add new cores: 'ri2bs' and 'mergecv'.
 
-* Support 'xovud' in 'xovpmx'.
+* Support setting more precise parameters in mutation and recombination operators.
 
-* A better EA framework with higher performance.
+* Support logging and showing log during the evoluation.
 
-* Add MOEA/D-DE algorithm template.
+* Speed up the EA framework.
 
 ## Installation
 1.Installing online:
@@ -139,35 +139,33 @@ if __name__ == '__main__':
     population = ea.Population(Encoding, Field, NIND) # Instantiate Population class(Just instantiate, not initialize the population yet.)
     """================================Algorithm set==============================="""
     myAlgorithm = ea.moea_NSGA3_templet(problem, population) # Instantiate a algorithm class.
-    myAlgorithm.MAXGEN = 500 # Set the max times of iteration.
-    """===============================Start evolution=============================="""
-    NDSet = myAlgorithm.run() # Run the algorithm templet.
-    """=============================Analyze the result============================="""
-    PF = problem.getReferObjV() # Get the global pareto front.
-    GD = ea.indicator.GD(NDSet.ObjV, PF) # Calculate GD
-    IGD = ea.indicator.IGD(NDSet.ObjV, PF) # Calculate IGD
-    HV = ea.indicator.HV(NDSet.ObjV, PF) # Calculate HV
-    Space = ea.indicator.Spacing(NDSet.ObjV) # Calculate Space
-    print('The number of non-dominated result: %s'%(NDSet.sizes))
-    print('GD: ',GD)
-    print('IGD: ',IGD)
-    print('HV: ', HV)
-    print('Space: ', Space)
+    myAlgorithm.MAXGEN = 500   # Set the max times of iteration.
+    myAlgorithm.logTras = 1    # Set the frequency of logging. If it is zero, it would not log.
+    myAlgorithm.verbose = True # Set if we want to print the log during the evolution or not.
+    myAlgorithm.drawing = 1    # 1 means draw the figure of the result.
+    """===============================Start evolution============================="""
+    [NDSet, population] = myAlgorithm.run()  # Run the algorithm templet.
+    """=============================Analyze the result============================"""
+    if myAlgorithm.log is not None and NDSet.sizes != 0:
+        print('GD', myAlgorithm.log['gd'][-1])
+        print('IGD', myAlgorithm.log['igd'][-1])
+        print('HV', myAlgorithm.log['hv'][-1])
+        print('Spacing', myAlgorithm.log['spacing'][-1])
 ```
 
-Run the "main.py" and the result is:
+Run the "main.py" and the part of the result is:
 
 ![image](https://github.com/geatpy-dev/geatpy/blob/master/geatpy/testbed/moea_test/moea_test_DTLZ/Pareto%20Front.svg)
 
 The number of non-dominated result: 91
 
-GD:  0.00019492736742063313
+GD 0.00022198303156041217
 
-IGD:  0.02058320808720775
+IGD 0.02068151005217868
 
-HV:  0.8413590788841248
+HV 0.8402294516563416
 
-Space:  0.00045742613969278813
+Spacing 0.00045354439805786744
 
 For solving another problem: **Ackley-30D**, which has only one object and 30 decision variables, what you need to do is almost the same as above.
 
@@ -218,28 +216,25 @@ if __name__ == '__main__':
     myAlgorithm.MAXGEN = 1000      # Set the max times of iteration.
     myAlgorithm.mutOper.F = 0.5    # Set the F of DE
     myAlgorithm.recOper.XOVR = 0.2 # Set the Cr of DE (Here it is marked as XOVR)
-    myAlgorithm.drawing = 1 # 1 means draw the figure of the result
+    myAlgorithm.logTras = 1        # Set the frequency of logging. If it is zero, it would not log.
+    myAlgorithm.verbose = True     # Set if we want to print the log during the evolution or not.
+    myAlgorithm.drawing = 1        # 1 means draw the figure of the result.
     """===============================Start evolution=============================="""
-    [population, obj_trace, var_trace] = myAlgorithm.run() # Run the algorithm templet.
-    """=============================Analyze the result============================="""
-    best_gen = np.argmin(obj_trace[:, 1]) # Get the best generation.
-    best_ObjV = np.min(obj_trace[:, 1])
-    print('The objective value of the best solution is: %s'%(best_ObjV))
-    print('Effective iteration times: %s'%(obj_trace.shape[0]))
-    print('The best generation is: %s'%(best_gen + 1))
+    [BestIndi, population] = myAlgorithm.run() # Run the algorithm templet.
+    """==============================Output the result============================="""
     print('The number of evolution is: %s'%(myAlgorithm.evalsNum))
+    if BestIndi.sizes != 0:
+        print('The objective value of the best solution is: %s' % BestIndi.ObjV[0][0])
+    else:
+        print('Did not find any feasible solution.')
 ```
 
-The result is:
+Part of the result is:
 
 ![image](https://github.com/geatpy-dev/geatpy/blob/master/geatpy/testbed/soea_test/soea_test_Ackley/result1.svg)
 
-The objective value of the best solution is: 5.8686921988737595e-09
-
-Effective iteration times: 1000
-
-The best generation is: 1000
-
 The number of evolution is: 20000
 
+The objective value of the best solution is: 2.7631678278794425e-08
+
 To get more tutorials, please link to http://www.geatpy.com.