Added FireFly Algorithm (FFA)

FFA.py

@@ -0,0 +1,146 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun May 29 00:49:35 2016
+
+@author: hossam
+"""
+
+#% ======================================================== % 
+#% Files of the Matlab programs included in the book:       %
+#% Xin-She Yang, Nature-Inspired Metaheuristic Algorithms,  %
+#% Second Edition, Luniver Press, (2010).   www.luniver.com %
+#% ======================================================== %    
+#
+#% -------------------------------------------------------- %
+#% Firefly Algorithm for constrained optimization using     %
+#% for the design of a spring (benchmark)                   % 
+#% by Xin-She Yang (Cambridge University) Copyright @2009   %
+#% -------------------------------------------------------- %
+
+import numpy
+import math
+import time
+from solution import solution
+
+
+
+
+
+def alpha_new(alpha,NGen):
+    #% alpha_n=alpha_0(1-delta)^NGen=10^(-4);
+    #% alpha_0=0.9
+    delta=1-(10**(-4)/0.9)**(1/NGen);
+    alpha=(1-delta)*alpha
+    return alpha
+
+
+
+def FFA(objf,lb,ub,dim,n,MaxGeneration):
+
+    #General parameters
+
+    #n=50 #number of fireflies
+    #dim=30 #dim  
+    #lb=-50
+    #ub=50
+    #MaxGeneration=500
+
+    #FFA parameters
+    alpha=0.5  # Randomness 0--1 (highly random)
+    betamin=0.20  # minimum value of beta
+    gamma=1   # Absorption coefficient
+
+
+
+    zn=numpy.ones(n)
+    zn.fill(float("inf")) 
+
+
+    #ns(i,:)=Lb+(Ub-Lb).*rand(1,d);
+    ns=numpy.random.uniform(0,1,(n,dim)) *(ub-lb)+lb
+    Lightn=numpy.ones(n)
+    Lightn.fill(float("inf")) 
+
+    #[ns,Lightn]=init_ffa(n,d,Lb,Ub,u0)
+
+    convergence=[]
+    s=solution()
+
+
+    print("CS is optimizing  \""+objf.__name__+"\"")    
+
+    timerStart=time.time() 
+    s.startTime=time.strftime("%Y-%m-%d-%H-%M-%S")
+
+    # Main loop
+    for k in range (0,MaxGeneration):     # start iterations
+
+        #% This line of reducing alpha is optional
+        alpha=alpha_new(alpha,MaxGeneration);
+
+        #% Evaluate new solutions (for all n fireflies)
+        for i in range(0,n):
+            zn[i]=objf(ns[i,:])
+            Lightn[i]=zn[i]
+
+
+
+
+        # Ranking fireflies by their light intensity/objectives
+
+
+        Lightn=numpy.sort(zn)
+        Index=numpy.argsort(zn)
+        ns=ns[Index,:]
+
+
+        #Find the current best
+        nso=ns
+        Lighto=Lightn
+        nbest=ns[0,:] 
+        Lightbest=Lightn[0]
+
+        #% For output only
+        fbest=Lightbest;
+
+        #% Move all fireflies to the better locations
+    #    [ns]=ffa_move(n,d,ns,Lightn,nso,Lighto,nbest,...
+    #          Lightbest,alpha,betamin,gamma,Lb,Ub);
+        scale=numpy.ones(dim)*abs(ub-lb)
+        for i in range (0,n):
+            # The attractiveness parameter beta=exp(-gamma*r)
+            for j in range(0,n):
+                r=numpy.sqrt(numpy.sum((ns[i,:]-ns[j,:])**2));
+                #r=1
+                # Update moves
+                if Lightn[i]>Lighto[j]: # Brighter and more attractive
+                   beta0=1
+                   beta=(beta0-betamin)*math.exp(-gamma*r**2)+betamin
+                   tmpf=alpha*(numpy.random.rand(dim)-0.5)*scale
+                   ns[i,:]=ns[i,:]*(1-beta)+nso[j,:]*beta+tmpf
+
+
+        #ns=numpy.clip(ns, lb, ub)
+
+        convergence.append(fbest)
+
+        IterationNumber=k
+        BestQuality=fbest
+
+        if (k%1==0):
+               print(['At iteration '+ str(k)+ ' the best fitness is '+ str(BestQuality)])
+    #    
+       ####################### End main loop
+    timerEnd=time.time()  
+    s.endTime=time.strftime("%Y-%m-%d-%H-%M-%S")
+    s.executionTime=timerEnd-timerStart
+    s.convergence=convergence
+    s.optimizer="FFA"
+    s.objfname=objf.__name__
+
+    return s
+
+
+
+
+