diff --git a/Optimization algorithms/pso.py b/Optimization algorithms/pso.py
deleted file mode 100644
index 0f0a71a..0000000
--- a/Optimization algorithms/pso.py	
+++ /dev/null
@@ -1,86 +0,0 @@
-import numpy as np 
-import math
-import matplotlib.pyplot as plt
-global nPop 
-nPop = 50 #swarm size
-global nVar
-nVar = 5; #five dimensional space: number of unknown variables
-varMin = -10 #lower bound
-varMax = 10 #upper bound
-varSize = np.array((1,nVar))  #matrix size of decision variables
-
-#particle template
-class particle:
-	def __init__(self,position,velocity,cost,best_pos,best_cost,global_best,global_best_pos):
-		self.position = np.ones((nPop,nVar))
-		self.velocity = np.zeros((nPop,nVar))
-		self.cost     = np.ones((nPop,1))
-		self.best_pos = np.zeros((nPop,nVar))
-		self.best_cost = np.ones((nPop,1))
-		#self.global_best = np.ndarray((nPop,pow(math.e,6)))
-		self.global_best = pow(10,6)
-		self.global_best_pos = 0
-
-#problem defintion
-def objective_function(x):
-	return np.multiply(x,x)		#cost function
-
-#parameters of PSO
-maxIt = 1000 #max iterations
-w = 1. #inertia coeff
-c1 = 2. #personal accelration coeff
-c2 = 2. #global acc coeff
-wdamp =0.99
-#nitialization
-
-swarm = particle(np.ones((nPop,nVar)),np.zeros((nPop,nVar)),0,np.ones((nPop,nVar)),np.ones((nPop,nVar)),0,0)
-
-
-for i in range(nPop):
-	#randomly initialise positions
-	swarm.position[i] = np.random.uniform(varMin,varMax,varSize)
-	swarm.velocity = np.zeros((nPop, nVar))
-	#calculate cost
-	swarm.cost[i] = objective_function(np.sum(swarm.position[i]))
-	#update personal best
-	swarm.best_pos[i] = swarm.position[i]
-	swarm.best_cost[i] = swarm.cost[i]
-	#update global best
-	if (swarm.best_cost[i] <= swarm.global_best):
-		swarm.global_best, swarm.global_best_pos = swarm.best_cost[i],i
-
-#aray to hold best cost value for each iteration 
-bestCosts = np.zeros((maxIt,1))
-#main loop of  PSO
-for it in range(maxIt):
-	for i in range(nPop):
-		swarm.velocity[i] = w*swarm.velocity[i] + c1*np.multiply((np.random.rand(1,nVar)),(swarm.best_pos[i]-swarm.position[i]))\
-							+ c2* np.multiply(np.random.rand(1,nVar),(swarm.global_best_pos-swarm.best_pos[i]))
-
-		swarm.position[i] += swarm.velocity[i]
-		swarm.cost[i] = objective_function(np.sum(swarm.position[i]))
-		if swarm.cost[i]<swarm.best_cost[i]:
-			swarm.best_pos[i] = swarm.position[i]
-			swarm.best_cost[i] = swarm.cost[i]
-
-			if (swarm.best_cost[i] <= swarm.global_best):
-				swarm.global_best, swarm.global_best_pos = swarm.best_cost[i],i
-			
-	#store best cost value and position
-	bestCosts[it] = swarm.global_best
-	#print "best_cost:",bestCosts[i], "iteration:",it
-
-	#damping inertia 
-	w = w*wdamp
-#results
-plt.xlabel('Iteration')
-plt.ylabel('Best Cost')
-plt.yscale('log')
-plt.xscale('log')
-plt.grid(True)
-plt.plot(bestCosts)
-plt.show()
-
-#print [swarm.global_best,swarm.global_best_pos]
-
-print np.ones((5,1))
\ No newline at end of file