def local_search_2(solution):
best_cost = calc_cost(solution)
best_sol = solution
for idx1 in range(num_jobs):
for idx2 in range(idx1 + 1, num_jobs):
sol = swap(idx1, idx2, solution)
sol_cost = calc_cost(sol)
if sol_cost < best_cost:
best_cost = sol_cost
best_sol = sol
return best_sol, best_cost
def local_search_2(solution):
best_cost = calc_cost(solution)
best_sol = solution
for idx1 in range(num_jobs):
for idx2 in range(idx1+1, num_jobs):
sol = swap(idx1, idx2, solution)
sol_cost = calc_cost(sol)
if sol_cost < best_cost:
best_cost = sol_cost
best_sol = sol
return best_sol, best_cost
def local_search(solution):
best_cost = calc_cost(solution)
best_sol = solution
for job_idx in range(num_jobs):
sub_sol = np.delete(solution,job_idx)
for idx in range(num_jobs):
if job_idx != idx:
sol = np.insert(sub_sol, idx, solution[job_idx])
sol_cost = calc_cost(sol)
if sol_cost < best_cost:
best_cost = sol_cost
best_sol = sol
return best_sol
def local_search(solution):
best_cost = calc_cost(solution)
best_sol = solution
for job_idx in range(num_jobs):
sub_sol = np.delete(solution, job_idx)
for idx in range(num_jobs):
if job_idx != idx:
sol = np.insert(sub_sol, idx, solution[job_idx])
sol_cost = calc_cost(sol)
if sol_cost < best_cost:
best_cost = sol_cost
best_sol = sol
return best_sol
def ils(num_iter):
initial_sol = best_insertion()
solution = local_search(initial_sol)
best_cost = calc_cost(solution)
for i in range(num_iter):
sol = perturbate(solution)
sol = local_search(sol)
sol_cost = calc_cost(sol)
if sol_cost < best_cost:
solution = sol
best_cost = sol_cost
return solution, best_cost
def best_insertion():
solution = np.array([], dtype=int)
unallocated = list(range(num_jobs))
for job_idx in range(num_jobs):
best_cost = sys.maxsize
for job in unallocated:
for pos in range(job_idx+1):
sol = np.insert(solution, pos, job)
cost = calc_cost(sol)
if cost < best_cost:
best_cost = cost
best_insertion = [job,pos]
job, pos = best_insertion
solution = np.insert(solution, pos, job)
unallocated.remove(job)
return solution, best_cost
def best_insertion():
solution = np.array([], dtype=int)
unallocated = list(range(num_jobs))
for job_idx in range(num_jobs):
best_cost = sys.maxsize
for job in unallocated:
for pos in range(job_idx + 1):
sol = np.insert(solution, pos, job)
cost = calc_cost(sol)
if cost < best_cost:
best_cost = cost
best_insertion = [job, pos]
job, pos = best_insertion
solution = np.insert(solution, pos, job)
unallocated.remove(job)
return solution, best_cost
def genetic(num_iter, num_sampl, mut_prob, local_prob):
population = build_initial_pop(num_sampl, num_jobs)
costs = calc_costs(population)
new_population = np.zeros((num_sampl, num_jobs), dtype=int)
new_costs = np.zeros(num_sampl, dtype=int)
for i in range(num_iter):
for j in range(num_sampl):
parent1, parent2 = select_couple(population, costs)
son = crossover(parent1, parent2)
if random() < mut_prob:
son = mutate(son)
if random() < local_prob:
son = local_search(son)
new_costs[j] = calc_cost(son)
new_population[j] = son
population, costs = update_population(population, costs, new_population, new_costs)
best_idx = np.argmin(costs)
return population[best_idx], costs[best_idx]
def genetic(num_iter, num_sampl, mut_prob, local_prob):
population = build_initial_pop(num_sampl, num_jobs)
costs = calc_costs(population)
new_population = np.zeros((num_sampl, num_jobs), dtype=int)
new_costs = np.zeros(num_sampl, dtype=int)
for i in range(num_iter):
for j in range(num_sampl):
parent1, parent2 = select_couple(population, costs)
son = crossover(parent1, parent2)
if random() < mut_prob:
son = mutate(son)
if random() < local_prob:
son = local_search(son)
new_costs[j] = calc_cost(son)
new_population[j] = son
population, costs = update_population(population, costs,
new_population, new_costs)
best_idx = np.argmin(costs)
return population[best_idx], costs[best_idx]
def calc_costs(samples):
return [calc_cost(sample) for sample in samples]
def calc_costs(samples):
return [ calc_cost(sample) for sample in samples ]
def accept_criteria(sol, best_cost):
return min(calc_cost(sol), best_cost)
