125 lines
3.4 KiB
Python
125 lines
3.4 KiB
Python
import random
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import numpy
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import time
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from .solution import solution
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# Differential Evolution (DE)
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# mutation factor = [0.5, 2]
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# crossover_ratio = [0,1]
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def DE(objf, lb, ub, dim, PopSize, iters):
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mutation_factor = 0.5
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crossover_ratio = 0.7
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stopping_func = None
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# convert lb, ub to array
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if not isinstance(lb, list):
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lb = [lb for _ in range(dim)]
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ub = [ub for _ in range(dim)]
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# solution
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s = solution()
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s.best = float("inf")
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# initialize population
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population = []
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population_fitness = numpy.array([float("inf") for _ in range(PopSize)])
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for p in range(PopSize):
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sol = []
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for d in range(dim):
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d_val = random.uniform(lb[d], ub[d])
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sol.append(d_val)
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population.append(sol)
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population = numpy.array(population)
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# calculate fitness for all the population
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for i in range(PopSize):
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fitness = objf(population[i, :])
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population_fitness[p] = fitness
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# s.func_evals += 1
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# is leader ?
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if fitness < s.best:
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s.best = fitness
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s.leader_solution = population[i, :]
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convergence_curve = numpy.zeros(iters)
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# start work
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print('DE is optimizing "' + objf.__name__ + '"')
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timerStart = time.time()
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s.startTime = time.strftime("%Y-%m-%d-%H-%M-%S")
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t = 0
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while t < iters:
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# should i stop
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if stopping_func is not None and stopping_func(s.best, s.leader_solution, t):
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break
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# loop through population
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for i in range(PopSize):
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# 1. Mutation
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# select 3 random solution except current solution
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ids_except_current = [_ for _ in range(PopSize) if _ != i]
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id_1, id_2, id_3 = random.sample(ids_except_current, 3)
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mutant_sol = []
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for d in range(dim):
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d_val = population[id_1, d] + mutation_factor * (
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population[id_2, d] - population[id_3, d]
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)
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# 2. Recombination
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rn = random.uniform(0, 1)
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if rn > crossover_ratio:
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d_val = population[i, d]
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# add dimension value to the mutant solution
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mutant_sol.append(d_val)
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# 3. Replacement / Evaluation
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# clip new solution (mutant)
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mutant_sol = numpy.clip(mutant_sol, lb, ub)
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# calc fitness
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mutant_fitness = objf(mutant_sol)
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# s.func_evals += 1
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# replace if mutant_fitness is better
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if mutant_fitness < population_fitness[i]:
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population[i, :] = mutant_sol
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population_fitness[i] = mutant_fitness
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# update leader
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if mutant_fitness < s.best:
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s.best = mutant_fitness
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s.leader_solution = mutant_sol
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convergence_curve[t] = s.best
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if t % 1 == 0:
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print(
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["At iteration " + str(t + 1) + " the best fitness is " + str(s.best)]
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)
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# increase iterations
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t = t + 1
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timerEnd = time.time()
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s.endTime = time.strftime("%Y-%m-%d-%H-%M-%S")
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s.executionTime = timerEnd - timerStart
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s.convergence = convergence_curve
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s.optimizer = "DE"
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s.bestIndividual = s.leader_solution
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s.objfname = objf.__name__
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# return solution
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return s
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