The PAO cometh

add PAO - Particle attractor algorithm

Author: MDCHAMP

.github/workflows/pytest-benchmarking.yml

@@ -10,7 +10,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.9]
+        python-version: [3.11]
 
     steps:
     - uses: actions/checkout@v2

.github/workflows/pytest-testing.yml

@@ -15,7 +15,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.5, 3.6, 3.7, 3.8, 3.9]
+        python-version: ['3.7', '3.8', '3.9', '3.10', '3.11']
 
     steps:
     - uses: actions/checkout@v2

.gitignore

@@ -150,4 +150,6 @@ local/
 *.old
 
 # Local sractch
-.misc/
+.misc/
+Paper
+fig.png

setup.py

@@ -8,7 +8,7 @@
 # This call to setup() does all the work
 setup(
     name="freelunch",
-    version="0.0.14",
+    version="0.0.15",
     description="Heuristic and meta-heuristic optimisation suite in Python",
     long_description=rm,
     long_description_content_type="text/markdown",

src/freelunch/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.0.14"
+__version__ = "0.0.15"
 
 from freelunch.optimisers import *
 import freelunch.benchmarks as benchmarks

src/freelunch/base.py

@@ -106,6 +106,12 @@ def _obj(self, obj, vec):
         except(ValueError, TypeError):
             return None
 
+    def curve_callback(self, best=None, mean=None):
+        if best is None:
+            self.data = {'best':[], 'mean':[]}
+        else:
+            self.data['best'].append(best)
+            self.data['mean'].append(mean)
 
 # Subclasses for granularity
 

src/freelunch/benchmarks.py

@@ -13,12 +13,10 @@ class benchmark:
     '''
     default_bounds = lambda n:None
     rtm_optimum = lambda n:None
+    tol=None
 
-    def __init__(self, n=None):
-        if n is None:
-            self.n = 2
-        else:
-            self.n = n
+    def __init__(self, n=2):
+        self.n = n
         self.bounds = self.default_bounds() 
         self.optimum = self.rtn_optimum()
 
@@ -27,8 +25,7 @@ def __call__(self, dna):
             raise ZeroLengthSolutionError('An empty trial solution was passed')
         return self.obj(dna)
 
-# %%
-
+# %% some misc (v0.x) benchmarks
 
 class ackley(benchmark):
     '''
@@ -97,3 +94,134 @@ def obj(self, dna):
         t2 = - 0.1 * np.exp(-np.sum(dna**2))
         return t1 + t2
 
+
+
+# %% https://robertmarks.org/Classes/ENGR5358/Papers/functions.pdf
+
+class DeJong(benchmark):
+    '''
+    DeJong's 1st function in n dimensions
+    '''
+    default_bounds = lambda self:np.array([[-5.12, 5.12]]*self.n)
+    rtn_optimum = lambda self:np.array([0]*self.n)
+    f0 = 0
+
+    def obj(self, dna):
+        return np.sum(dna**2)
+
+class HyperElipsoid(benchmark):
+    '''
+    HyperElipsoid function in n dimensions
+    '''
+    default_bounds = lambda self:np.array([[-5.12, 5.12]]*self.n)
+    rtn_optimum = lambda self:np.array([0]*self.n)
+    f0 = 0
+
+    def obj(self, dna):
+        return np.sum(np.arange(1,self.n+1)*dna**2)
+
+class RotatedHyperElipsoid(benchmark):
+    '''
+    RotatedHyperElipsoid function in n dimensions
+    '''
+    default_bounds = lambda self:np.array([[-65.536, 65.536]]*self.n)
+    rtn_optimum = lambda self:np.array([0]*self.n)
+    f0 = 0
+
+    def obj(self, dna):
+        out = 0
+        for i in range(self.n):
+            out += np.sum(dna[:i+1]**2)
+        return out
+
+class Rosenbrock(benchmark):
+    '''
+    Rosenbrock's function in n dimensions (banana function)
+    '''
+    default_bounds = lambda self:np.array([[-2.048, 2.048]]*self.n)
+    rtn_optimum = lambda self:np.array([1]*self.n)
+    f0 = 0
+
+    def obj(self, dna):
+        return np.sum(100*(dna[1:] - dna[:-1]**2)**2 + (1-dna[:-1])**2)
+        
+
+class Ragstrin(benchmark):
+    '''
+    Ragstrin's function in n dimensions 
+    '''
+    default_bounds = lambda self:np.array([[-5.12, 5.12]]*self.n)
+    rtn_optimum = lambda self:np.array([0]*self.n)
+    f0 = 0
+    
+    def obj(self, dna):
+        return 10 * self.n + np.sum(dna**2 - 10*np.cos(2* np.pi*dna))
+        
+class Schwefel(benchmark):
+    '''
+    Schwefel's function in n dimensions
+
+    (divided through by dimension for constant f0)
+    '''
+    default_bounds = lambda self:np.array([[-500, 500]]*self.n)
+    rtn_optimum = lambda self:np.array([420.9687]*self.n)
+    f0 = 0
+    
+    def obj(self, dna):
+        return 418.9828872721625-np.sum(dna*np.sin(np.sqrt(np.abs(dna))))/self.n
+        
+class Griewangk(benchmark):
+    '''
+    Griewangk's function in n dimensions
+
+    '''
+    default_bounds = lambda self:np.array([[-600, 600]]*self.n)
+    rtn_optimum = lambda self:np.array([0]*self.n)
+    f0 = 0
+    
+    def obj(self, dna):
+        return (1/4000)* np.sum(dna**2) - np.prod(np.cos(dna/np.sqrt(np.arange(self.n)+1))) + 1 
+
+class PowerSum(benchmark):
+    '''
+    Powersum function in n dimensions
+
+    '''
+    default_bounds = lambda self:np.array([[-1, 1]]*self.n)
+    rtn_optimum = lambda self:np.array([0]*self.n)
+    f0 = 0
+    
+    def obj(self, dna):
+        out = 0
+        for i,x in enumerate(dna):
+            out+=np.abs(x)**(i+1) 
+        return out
+
+class Ackley(benchmark):
+    '''
+    Ackely function in n dimensions
+    '''
+
+    default_bounds = lambda self:np.array([[-32.768, 32.768]]*self.n)
+    rtn_optimum = lambda self:np.array([0]*self.n)
+    f0 = 0
+
+    a,b,c = 20, 0.2, 2*np.pi
+    def obj(self, dna):
+        t1 = -self.a * np.exp(-self.b * (1/len(dna)) * np.sum(dna**2))
+        t2 = - np.exp(1/len(dna) * np.sum(np.cos(self.c * dna))) 
+        t3 = self.a + np.exp(1)
+        return t1 + t2 + t3   
+
+
+MOLGA_TEST_SUITE = [
+    DeJong,
+    HyperElipsoid,
+    RotatedHyperElipsoid,
+    Rosenbrock,
+    Ragstrin,
+    Schwefel,
+    Griewangk,
+    PowerSum,
+    Ackley
+]