def _refine_solution(sol, D, d, C, L, minimize_K):
# refine until stuck at a local optima
local_optima_reached = False
while not local_optima_reached:
sol = without_empty_routes(sol)
if not minimize_K:
sol.append(0) #make sure there is an empty route to move the pt to
# improve with relocation and keep 2-optimal
sol = do_local_search([do_1point_move, do_2opt_move], sol, D, d, C, L,
LSOPT.BEST_ACCEPT)
# try to redistribute the route with smallest demand
sol = without_empty_routes(sol)
routes = RouteData.from_solution(sol, D, d)
min_rd = min(routes, key=lambda rd: rd.demand)
routes.remove(min_rd)
if not minimize_K:
routes.append(RouteData())
if __debug__:
log(
DEBUG, "Applying do_redistribute_move on %s (%.2f)" %
(str(sol), objf(sol, D)))
redisribute_result = do_redistribute_move(
min_rd,
routes,
D,
d,
C,
L,
strategy=LSOPT.FIRST_ACCEPT,
#Note: Mole and Jameson do not specify exactly
# how the redistribution is done (how many
# different combinations are tried).
# Increase the recombination_level if the
# for more agressive and time consuming search
# for redistributing the customers on other
# routes.
recombination_level=0)
redisribute_delta = redisribute_result[-1]
if (redisribute_delta is not None) and\
(minimize_K or redisribute_delta<0.0):
updated_sol = RouteData.to_solution(redisribute_result[:-1])
if __debug__:
log(DEBUG - 1,
("Improved from %s (%.2f) to %s (%.2f)" %
(sol, objf(sol, D), updated_sol, objf(updated_sol, D))) +
"using inter route heuristic do_redistribute_move\n")
sol = updated_sol
else:
local_optima_reached = True
if __debug__:
log(DEBUG - 1, "No move with do_redistribute_move\n")
return sol
def do_local_search(ls_ops, sol, D, d, C, L=None,
operator_strategy=LSOPT.FIRST_ACCEPT,
iteration_strategy=ITEROPT.ALL_ACCEPT,
max_iterations=None):
""" Repeatedly apply ls_ops until no more improvements can be made. The
procedure keeps track of the changed routes and searches only combinations
that have been changed.
Optionally the operator_strategy FIRST_ACCEPT (default)/BEST_ACCEPT can be
given as well as the maximum number of iterations (that is, how many times
all given operations are applied until giving up on reaching local optima).
The iteration_strategy has an effect on which order the operations
are applied. If ALL_ACCEPT (default), each operator is applied in turn
until no improving moves are found. The options are:
* FIRST_ACCEPT accept every improving move returned by the operator, and
start again from the first operator.
* BEST_ACCEPT accept the very best (single) move over all operators.
* ALL_ACCEPT accept every improving move of each operator and continue.
* REPEATED_ACCEPT run operator until no improving moves are found before
moving on to the next operator.
Note that these may freely be combined with the operator_strategy.
"""
current_sol = sol
route_datas = RouteData.from_solution(sol, D, d)
route_data_idxs = list(range(len(route_datas)))
# We keep track of the operations to avoid search when it has already been
# unsuccesfully applied
at_lsop_optimal = defaultdict(set)
customer_to_at_lsopt_optimal = defaultdict(list)
iteration = 0
improving_iteration = True
while improving_iteration:
improving_iteration = False
best_iteration_result = None
best_iteration_delta = None
best_iteration_operator = None
ls_op_idx = 0
while ls_op_idx<len(ls_ops):
ls_op = ls_ops[ls_op_idx]
ls_op_args = getargspec(ls_op)[0]
route_count = ls_op_args.index('D')
op_order_sensitive = ls_op in ROUTE_ORDER_SENSITIVE_OPERATORS
op_improved = False
if __debug__:
log(DEBUG-1, "Applying %s on %s"%(ls_op.__name__, str(current_sol)))
# TODO: Consider using a counter to check for this
# check if we already reached local optima on all routes with ls_op
#if all( (ls_op in lsop_optimal[ri]) for ri in route_data_idxs ):
# if __debug__:
# log(DEBUG-2, "All route combinations already searched for %s, skipping it."%ls_op.__name__)
# break
best_delta = None
best_result = None
no_improving_lsop_found = set()
for route_indices in permutations(route_data_idxs,route_count):
# If the order does not matter, require that the route indices
# are ordered from smallest to largest.
if (not op_order_sensitive) and (not is_sorted(route_indices)):
continue
# ls_op is already at local optima with this combination of routes
if ls_op in at_lsop_optimal[route_indices]:
if __debug__:
log(DEBUG-2, "Route combination %s already searched for %s, skipping it."%
(str(route_indices), ls_op.__name__))
continue
# The one route case has different call signature
if route_count==1:
op_params = [route_datas[route_indices[0]].route,
D, operator_strategy]
else:
op_params = [route_datas[ri] for ri in route_indices]+\
[D, d, C, L, operator_strategy]
# Ideally, best_delta can be used as an upper
# bound to avoid unnecessary result generation
# and to allow early ls_op termination.
# However, then we lose the ability to mark
# some route combinations as ls_optimal.
#+[best_delta]
result = ls_op(*op_params)
#print("REMOVEME:",route_datas[route_indices[0]].route, "->", result)
# route was changed, record the change in route datas
delta = result[-1]
if delta is None:
no_improving_lsop_found.update((route_indices,))
else:
# For route_count==1 every route contributes for the same
# best_delta (unless trying to find the very best *single*
# move!)
if route_count==1:
skip_result = (
(best_delta != None and delta+S_EPS>best_delta) and
(iteration_strategy==ITEROPT.BEST_ACCEPT) )
if not skip_result:
if ((best_result is None) or
(iteration_strategy==ITEROPT.BEST_ACCEPT)):
best_result = []
best_delta = 0
old_rd = route_datas[route_indices[0]]
new_rd = RouteData(result[0],old_rd.cost+delta,old_rd.demand)
best_result.append( (route_indices[0], new_rd) )
best_delta+=delta
else:
if (best_result is None) or (delta+S_EPS<best_delta):
best_result = zip(route_indices, result[:-1])
best_delta = delta
# Found a first improving with this operator, move on.
if operator_strategy==LSOPT.FIRST_ACCEPT:
break # route combination loop
# end route combination loop
# Mark the routes that had no potential improvements to be at
# local optima to avoid checking the same moves again.
for ris in no_improving_lsop_found:
at_lsop_optimal[ris].add(ls_op)
for ri in ris:
customer_to_at_lsopt_optimal[ri].append(ris)
if best_result is not None:
if iteration_strategy==ITEROPT.BEST_ACCEPT:
if (best_iteration_result is None) or \
(best_delta+S_EPS<best_iteration_delta):
best_iteration_result = best_result
best_iteration_delta = best_delta
best_iteration_operator = ls_op.__name__
else:
op_improved = True
improving_iteration = True
for ri, new_rd in best_result:
route_datas[ri] = new_rd
# The route was modified, allow other operators to
# check if it can be improved again.
for ris in customer_to_at_lsopt_optimal[ri]:
at_lsop_optimal[ris].clear()
# Check if route is [0,0] or [0] or []
if len(new_rd.route)<=2:
# remove this route from the future search
route_data_idxs.remove(ri)
if __debug__:
op_improved = True
opt_sol = RouteData.to_solution(route_datas)
log(DEBUG, "Improved from %s (%.2f) to %s (%.2f) using %s"%
(str(current_sol),objf(current_sol,D),str(opt_sol),objf(opt_sol,D),ls_op.__name__))
current_sol = opt_sol
if iteration_strategy==ITEROPT.FIRST_ACCEPT:
ls_op_idx = 0
break # the ls_op loop (start from the beginning)
if __debug__:
if best_result is None:
log(DEBUG-1, "No improving move with %s"%ls_op.__name__)
if op_improved and iteration_strategy==ITEROPT.FIRST_ACCEPT:
# after an improvement start from the first operator
ls_op_idx = 0
if op_improved and iteration_strategy==ITEROPT.REPEATED_ACCEPT:
# keep repeating the operator until no improvement is found
ls_op_idx = ls_op_idx
else:
# BEST_ACCEPT and ALL_ACCEPT always move on
ls_op_idx += 1
#END OF LS_OP LOOP
if (iteration_strategy==ITEROPT.BEST_ACCEPT) and\
(best_iteration_result is not None):
improving_iteration = True
for ri, new_rd in best_iteration_result:
route_datas[ri] = new_rd
# The route was modified, allow other operators to
# check if it can be improved again.
for ris in customer_to_at_lsopt_optimal[ri]:
at_lsop_optimal[ris].clear()
# Check if route is [0,0] or [0] or []
if len(new_rd.route)<=2:
# remove this route from the future search
route_data_idxs.remove(ri)
if __debug__:
op_improved = True
opt_sol = RouteData.to_solution(route_datas)
log(DEBUG, "Improved from %s (%.2f) to %s (%.2f) using %s"%
(str(current_sol),objf(current_sol,D),str(opt_sol),objf(opt_sol,D),best_iteration_operator))
current_sol = opt_sol
iteration+=1
if max_iterations and iteration>=max_iterations:
break # iteration loop
current_sol = RouteData.to_solution(route_datas)
if __debug__:
log(DEBUG,"Repeadedly applying %s resulted in %s"%
(",".join(ls_op.__name__ for ls_op in ls_ops),str(current_sol)))
return current_sol