Alpha_pos = np.zeros(dim)
Alpha_score = float("inf")
Beta_pos = np.zeros(dim)
Beta_score = float("inf")
Delta_pos = np.zeros(dim)
Delta_score = float("inf")
#Initialize the positions of search agents
population = []
for i in range(SearchAgents_no):
vector = np.random.uniform(low=lb, high=ub, size=dim)
fitness = structureEnergy(transform(vector), aminoAcid)
if fitness < Alpha_score:
Delta_score = Beta_score
Delta_pos = Beta_pos
Beta_score = Alpha_score
Beta_pos = Alpha_pos
Alpha_score = fitness
Alpha_pos = vector
if (fitness > Alpha_score and fitness < Beta_score):
Delta_score = Beta_score
Delta_pos = Beta_pos
Beta_score = fitness
Beta_pos = vector
inertiaWeight = 0.7
hypersphereRadius = 7
c1 = 1.49618
c2 = 1.49618
c3 = 1.01
gbestPositionVector = np.zeros(dimension)
gbestFitness = sys.maxsize
gbestParticle = -1
for i in range(populationSize):
vector = np.random.uniform(low=posMin, high=posMax, size=dimension)
fitness = structureEnergy(transform(vector), aminoAcid)
population.append(
particle(vector, np.zeros(dimension), fitness, hypersphereRadius))
if fitness < gbestFitness:
gbestPositionVector = vector
gbestFitness = fitness
gbestParticle = i
gbestFitnessAtEachIteration = []
print("%d %d" % (0, gbestFitness))
gbestFitnessAtEachIteration.append(gbestFitness)
for it in range(maxIterations):
dampingFactor = 1.0
c1 = 2
c2 = 2
#Initialisation
population = []
gbestPositionVector = np.zeros(dimension)
gbestCost = sys.maxsize
for i in range(populationSize):
vector = np.random.uniform(low=posMin, high=posMax, size=dimension)
fitness = structureEnergy(transform(vector), aminoAcid)
population.append(particle(vector, np.zeros(dimension), fitness))
if fitness < gbestCost:
gbestPositionVector = vector
gbestCost = fitness
gbestCostAtEachIteration = []
gbestCostAtEachIteration.append(gbestCost)
print("%d %d" % (0, gbestCost))
#Main Loop starts
for it in range(maxIterations):
Alpha_pos = np.zeros(dim)
Alpha_score = float("inf")
Beta_pos = np.zeros(dim)
Beta_score = float("inf")
Delta_pos = np.zeros(dim)
Delta_score = float("inf")
#Initialize the positions of search agents
population = []
for i in range(SearchAgents_no):
vector = np.random.uniform(low=lb, high=ub, size=dim)
fitness = structureEnergy(transform(vector), aminoAcid)
if fitness < Alpha_score:
Delta_score = Beta_score
Delta_pos = Beta_pos
Beta_score = Alpha_score
Beta_pos = Alpha_pos
Alpha_score = fitness
Alpha_pos = vector
if (fitness > Alpha_score and fitness < Beta_score):
Delta_score = Beta_score
Delta_pos = Beta_pos
Beta_score = fitness
Beta_pos = vector